{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T18:13:39Z","timestamp":1783188819033,"version":"3.54.6"},"reference-count":157,"publisher":"Springer Science and Business Media LLC","issue":"S1","license":[{"start":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T00:00:00Z","timestamp":1690416000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T00:00:00Z","timestamp":1690416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62173249"],"award-info":[{"award-number":["62173249"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62173249"],"award-info":[{"award-number":["62173249"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62173249"],"award-info":[{"award-number":["62173249"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62173249"],"award-info":[{"award-number":["62173249"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62173249"],"award-info":[{"award-number":["62173249"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62173249"],"award-info":[{"award-number":["62173249"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62173249"],"award-info":[{"award-number":["62173249"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"publisher","award":["20ZR1460100"],"award-info":[{"award-number":["20ZR1460100"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"publisher","award":["20ZR1460100"],"award-info":[{"award-number":["20ZR1460100"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"publisher","award":["20ZR1460100"],"award-info":[{"award-number":["20ZR1460100"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"publisher","award":["20ZR1460100"],"award-info":[{"award-number":["20ZR1460100"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"publisher","award":["20ZR1460100"],"award-info":[{"award-number":["20ZR1460100"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"publisher","award":["20ZR1460100"],"award-info":[{"award-number":["20ZR1460100"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"publisher","award":["20ZR1460100"],"award-info":[{"award-number":["20ZR1460100"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s10462-023-10558-5","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T12:02:23Z","timestamp":1690459343000},"page":"1417-1477","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":79,"title":["Siamese object tracking for unmanned aerial vehicle: a review and comprehensive analysis"],"prefix":"10.1007","volume":"56","author":[{"given":"Changhong","family":"Fu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunhan","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangze","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjie","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziang","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bowen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geng","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,27]]},"reference":[{"issue":"5","key":"10558_CR1","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1007\/s00371-020-01848-y","volume":"37","author":"MY Abbass","year":"2021","unstructured":"Abbass MY, Kwon KC, Kim N et al. (2021) A survey on online learning for visual tracking. Vis Comput 37(5):993\u20131014. https:\/\/doi.org\/10.1007\/s00371-020-01848-y","journal-title":"Vis Comput"},{"issue":"5","key":"10558_CR2","doi-asserted-by":"publisher","first-page":"3887","DOI":"10.1007\/s10462-020-09943-1","volume":"54","author":"Y Akbari","year":"2021","unstructured":"Akbari Y, Almaadeed N, Al-Maadeed S et al. (2021) Applications, databases and open computer vision research from drone videos and images: a survey. Artif Intell Rev 54(5):3887\u20133938. https:\/\/doi.org\/10.1007\/s10462-020-09943-1","journal-title":"Artif Intell Rev"},{"key":"10558_CR3","doi-asserted-by":"publisher","unstructured":"Baykara HC, B\u0131y\u0131k E, G\u00fcl G et al. (2017) Real-time detection, tracking and classification of multiple moving objects in UAV videos. In: Proceedings of the international conference on tools with artificial intelligence (ICTAI), pp 945\u2013950. https:\/\/doi.org\/10.1109\/ICTAI.2017.00145","DOI":"10.1109\/ICTAI.2017.00145"},{"key":"10558_CR4","unstructured":"Bertinetto L, Henriques JF, Valmadre J et al. (2016a) Learning feed-forward one-shot learners. In: Proceedings of the advances in neural information processing systems (NeurIPS), pp 1\u20139"},{"key":"10558_CR5","doi-asserted-by":"publisher","unstructured":"Bertinetto L, Valmadre J, Henriques JF, et al. (2016b) Fully-convolutional Siamese networks for object tracking. In: Proceedings of the European conference on computer vision workshops (ECCVW), pp 850\u2013865. https:\/\/doi.org\/10.1007\/978-3-319-48881-3_56","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"10558_CR6","doi-asserted-by":"publisher","unstructured":"Bhat G, Danelljan M, Van\u00a0Gool L et al. (2019) Learning discriminative model prediction for tracking. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 6181\u20136190. https:\/\/doi.org\/10.1109\/ICCV.2019.00628","DOI":"10.1109\/ICCV.2019.00628"},{"key":"10558_CR7","doi-asserted-by":"crossref","unstructured":"Bromley J, Guyon I, LeCun Y et al. (1993) Signature verification using a \"Siamese\" time delay neural network. In: Proceedings of the advances in neural information processing systems (NeurIPS), pp 1\u20138","DOI":"10.1142\/9789812797926_0003"},{"key":"10558_CR8","doi-asserted-by":"publisher","unstructured":"Cai Z, Vasconcelos N (2018) Cascade R-CNN: delving into high quality object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 6154\u20136162. https:\/\/doi.org\/10.1109\/CVPR.2018.00644","DOI":"10.1109\/CVPR.2018.00644"},{"key":"10558_CR12","doi-asserted-by":"publisher","unstructured":"Cao Y, Xu J, Lin S et al. (2019) GCNet: non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of the IEEE\/CVF international conference on computer vision workshops (ICCVW), pp 1971\u20131980. https:\/\/doi.org\/10.1109\/ICCVW.2019.00246","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"10558_CR9","doi-asserted-by":"publisher","unstructured":"Cao Z, Fu C, Ye J et al. (2021a) HiFT: hierarchical feature Transformer for aerial tracking. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 15437\u201315446. https:\/\/doi.org\/10.1109\/ICCV48922.2021.01517","DOI":"10.1109\/ICCV48922.2021.01517"},{"key":"10558_CR10","doi-asserted-by":"publisher","unstructured":"Cao Z, Fu C, Ye J et al. (2021b) SiamAPN++: Siamese attentional aggregation network for real-time UAV tracking. In: Proceedings of the IEEE\/RSJ international conference on intelligent robots and systems (IROS), pp 3086\u20133092. https:\/\/doi.org\/10.1109\/IROS51168.2021.9636309","DOI":"10.1109\/IROS51168.2021.9636309"},{"key":"10558_CR11","doi-asserted-by":"publisher","unstructured":"Cao Z, Huang Z, Pan L et al. (2022) TCTrack: temporal contexts for aerial tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 14778\u201314788. https:\/\/doi.org\/10.1109\/CVPR52688.2022.01438","DOI":"10.1109\/CVPR52688.2022.01438"},{"key":"10558_CR13","doi-asserted-by":"publisher","unstructured":"Carion N, Massa F, Synnaeve G et al. (2020) End-to-end object detection with Transformers. In: Proceedings of the European conference on computer vision (ECCV), pp 213\u2013229. https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"10558_CR18","doi-asserted-by":"publisher","unstructured":"Chen P, Zhou Y (2019) The review of target tracking for UAV. In: Proceedings of the IEEE conference on industrial electronics and applications (ICIEA), pp 1800\u20131805. https:\/\/doi.org\/10.1109\/ICIEA.2019.8833668","DOI":"10.1109\/ICIEA.2019.8833668"},{"issue":"4","key":"10558_CR14","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2018","unstructured":"Chen LC, Papandreou G, Kokkinos I et al. (2018) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRfs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848. https:\/\/doi.org\/10.1109\/TPAMI.2017.2699184","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10558_CR15","doi-asserted-by":"publisher","unstructured":"Chen X, Yan X, Zheng F et al. (2020a) One-shot adversarial attacks on visual tracking with dual attention. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 10173\u201310182, https:\/\/doi.org\/10.1109\/CVPR42600.2020.01019","DOI":"10.1109\/CVPR42600.2020.01019"},{"key":"10558_CR17","doi-asserted-by":"publisher","unstructured":"Chen Z, Zhong B, Li G et al. (2020b) Siamese box adaptive network for visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 6667\u20136676. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00670","DOI":"10.1109\/CVPR42600.2020.00670"},{"key":"10558_CR16","doi-asserted-by":"publisher","unstructured":"Chen X, Yan B, Zhu J et al. (2021) Transformer tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 8122\u20138131. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00803","DOI":"10.1109\/CVPR46437.2021.00803"},{"key":"10558_CR19","doi-asserted-by":"publisher","unstructured":"Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 1800\u20131807. https:\/\/doi.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"10558_CR21","doi-asserted-by":"publisher","unstructured":"Dai J, Qi H, Xiong Y et al. (2017) Deformable convolutional networks. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 764\u2013773. https:\/\/doi.org\/10.1109\/ICCV.2017.89","DOI":"10.1109\/ICCV.2017.89"},{"key":"10558_CR20","doi-asserted-by":"publisher","unstructured":"Dai Z, Cai B, Lin Y et al. (2021) UP-DETR: unsupervised pre-training for object detection with Transformers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 1601\u20131610. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00165","DOI":"10.1109\/CVPR46437.2021.00165"},{"key":"10558_CR22","doi-asserted-by":"publisher","unstructured":"Danelljan M, Bhat G, Khan FS et al. (2019) ATOM: accurate tracking by overlap maximization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 4655\u20134664. https:\/\/doi.org\/10.1109\/CVPR.2019.00479","DOI":"10.1109\/CVPR.2019.00479"},{"key":"10558_CR23","doi-asserted-by":"publisher","unstructured":"Danelljan M, Van\u00a0Gool L, Timofte R (2020) Probabilistic regression for visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 7181\u20137190. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00721","DOI":"10.1109\/CVPR42600.2020.00721"},{"issue":"1","key":"10558_CR24","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","volume":"134","author":"PT De Boer","year":"2005","unstructured":"De Boer PT, Kroese DP, Mannor S et al. (2005) A tutorial on the cross-entropy method. Ann Oper Res 134(1):19\u201367. https:\/\/doi.org\/10.1007\/s10479-005-5724-z","journal-title":"Ann Oper Res"},{"key":"10558_CR25","doi-asserted-by":"publisher","unstructured":"Dong X, Shen J (2018) Triplet loss in Siamese network for object tracking. In: Proceedings of the European conference on computer vision (ECCV), pp 459\u2013474. https:\/\/doi.org\/10.1007\/978-3-030-01261-8_28","DOI":"10.1007\/978-3-030-01261-8_28"},{"key":"10558_CR26","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A et al. (2020) An image is worth 16X16 words: Transformers for image recognition at scale. In: Proceedings of the international conference on learning representations (ICLR), pp 1\u201322"},{"key":"10558_CR27","doi-asserted-by":"publisher","unstructured":"Du D, Qi Y, Yu H et al. (2018) The unmanned aerial vehicle benchmark: object detection and tracking. In: Proceedings of the European conference on computer vision (ECCV), pp 370\u2013386. https:\/\/doi.org\/10.1007\/978-3-030-01249-6_23","DOI":"10.1007\/978-3-030-01249-6_23"},{"key":"10558_CR28","doi-asserted-by":"publisher","unstructured":"Elloumi M, Dhaou R, Escrig B et al. (2018) Monitoring road traffic with a UAV-based system. In: Proceedings of the IEEE wireless communications and networking conference (WCNC), pp 1\u20136. https:\/\/doi.org\/10.1109\/WCNC.2018.8377077","DOI":"10.1109\/WCNC.2018.8377077"},{"key":"10558_CR29","doi-asserted-by":"publisher","unstructured":"Fan H, Ling H (2019) Siamese cascaded region proposal networks for real-time visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 7944\u20137953. https:\/\/doi.org\/10.1109\/CVPR.2019.00814","DOI":"10.1109\/CVPR.2019.00814"},{"key":"10558_CR30","doi-asserted-by":"publisher","unstructured":"Fan H, Wen L, Du D et al. (2020) VisDrone-SOT2020: the vision meets drone single-object tracking challenge results. In: Proceedings of the European conference on computer vision (ECCV), pp 728\u2013749. https:\/\/doi.org\/10.1007\/978-3-030-66823-5_44","DOI":"10.1007\/978-3-030-66823-5_44"},{"issue":"1","key":"10558_CR31","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/s10462-018-9653-z","volume":"53","author":"MM Ferdaus","year":"2020","unstructured":"Ferdaus MM, Anavatti SG, Pratama M et al. (2020) Towards the use of fuzzy logic systems in rotary wing unmanned aerial vehicle: a review. Artif Intell Rev 53(1):257\u2013290. https:\/\/doi.org\/10.1007\/s10462-018-9653-z","journal-title":"Artif Intell Rev"},{"issue":"2","key":"10558_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3309665","volume":"52","author":"M Fiaz","year":"2019","unstructured":"Fiaz M, Mahmood A, Javed S et al. (2019) Handcrafted and deep trackers: recent visual object tracking approaches and trends. ACM Comput Surv 52(2):1\u201344. https:\/\/doi.org\/10.1145\/3309665","journal-title":"ACM Comput Surv"},{"key":"10558_CR41","doi-asserted-by":"publisher","unstructured":"Fu J, Liu J, Tian H et al. (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 3141\u20133149. https:\/\/doi.org\/10.1109\/CVPR.2019.00326","DOI":"10.1109\/CVPR.2019.00326"},{"issue":"12","key":"10558_CR33","doi-asserted-by":"publisher","first-page":"8940","DOI":"10.1109\/TGRS.2020.2992301","volume":"58","author":"C Fu","year":"2020","unstructured":"Fu C, Xu J, Lin F et al. (2020) Object saliency-aware dual regularized correlation filter for real-time aerial tracking. IEEE Trans Geosci Remote Sens 58(12):8940\u20138951. https:\/\/doi.org\/10.1109\/TGRS.2020.2992301","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"10558_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3083880","volume":"60","author":"C Fu","year":"2021","unstructured":"Fu C, Cao Z, Li Y et al. (2021a) Onboard real-time aerial tracking with efficient Siamese anchor proposal network. IEEE Trans Geosci Remote Sens 60:1\u201313. https:\/\/doi.org\/10.1109\/TGRS.2021.3083880","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"10558_CR39","doi-asserted-by":"publisher","unstructured":"Fu C, Cao Z, Li Y et al. (2021b) Siamese anchor proposal network for high-speed aerial tracking. In: Proceedings of the IEEE international conference on robotics and automation (ICRA), pp 510\u2013516. https:\/\/doi.org\/10.1109\/ICRA48506.2021.9560756","DOI":"10.1109\/ICRA48506.2021.9560756"},{"issue":"104","key":"10558_CR35","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.engappai.2020.104116","volume":"98","author":"C Fu","year":"2021","unstructured":"Fu C, Ding F, Li Y et al. (2021c) Learning dynamic regression with automatic distractor repression for real-time UAV tracking. Eng Appl Artif Intell 98(104):116. https:\/\/doi.org\/10.1016\/j.engappai.2020.104116","journal-title":"Eng Appl Artif Intell"},{"issue":"8","key":"10558_CR36","doi-asserted-by":"publisher","first-page":"6301","DOI":"10.1109\/TGRS.2020.3030265","volume":"59","author":"C Fu","year":"2021","unstructured":"Fu C, Ye J, Xu J et al. (2021d) Disruptor-aware interval-based response inconsistency for correlation filters in real-time aerial tracking. IEEE Trans Geosci Remote Sens 59(8):6301\u20136313. https:\/\/doi.org\/10.1109\/TGRS.2020.3030265","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"10558_CR40","doi-asserted-by":"publisher","unstructured":"Fu C, Dong H, Ye J et al. (2022a) HighlightNet: highlighting low-light potential features for real-time UAV tracking. In: Proceedings of the IEEE\/RSJ international conference on intelligent robots and systems (IROS), pp 12146\u201312153. https:\/\/doi.org\/10.1109\/IROS47612.2022.9981070","DOI":"10.1109\/IROS47612.2022.9981070"},{"issue":"1","key":"10558_CR37","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1109\/MGRS.2021.3072992","volume":"10","author":"C Fu","year":"2022","unstructured":"Fu C, Li B, Ding F et al. (2022b) Correlation filters for unmanned aerial vehicle-based aerial tracking: a review and experimental evaluation. IEEE Geosci Remote Sens Mag 10(1):125\u2013160. https:\/\/doi.org\/10.1109\/MGRS.2021.3072992","journal-title":"IEEE Geosci Remote Sens Mag"},{"key":"10558_CR42","doi-asserted-by":"publisher","unstructured":"Fu C, Li S, Yuan X et al. (2022c) Ad2Attack: adaptive adversarial attack on real-time UAV tracking. In: Proceedings of the IEEE international conference on robotics and automation (ICRA), pp 5893\u20135899. https:\/\/doi.org\/10.1109\/ICRA46639.2022.9812056","DOI":"10.1109\/ICRA46639.2022.9812056"},{"key":"10558_CR38","doi-asserted-by":"crossref","unstructured":"Fu C, Cai M, Li S et al. (2023) Continuity-aware latent interframe information mining for reliable UAV tracking, In: Proceedings of the IEEE international conference on robotics and automation (ICRA), pp 1327\u20131333. https:\/\/doi.org\/10.1109\/ICRA48891.2023.10160673","DOI":"10.1109\/ICRA48891.2023.10160673"},{"key":"10558_CR43","doi-asserted-by":"publisher","unstructured":"Gao J, Zhang T, Xu C (2019) Graph convolutional tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 4644\u20134654. https:\/\/doi.org\/10.1109\/CVPR.2019.00478","DOI":"10.1109\/CVPR.2019.00478"},{"key":"10558_CR44","doi-asserted-by":"publisher","unstructured":"Girshick R (2015) Fast R-CNN. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 1440\u20131448. https:\/\/doi.org\/10.1109\/ICCV.2015.169","DOI":"10.1109\/ICCV.2015.169"},{"issue":"1","key":"10558_CR45","doi-asserted-by":"publisher","first-page":"97","DOI":"10.3390\/s16010097","volume":"16","author":"LF Gonzalez","year":"2016","unstructured":"Gonzalez LF, Montes GA, Puig E et al. (2016) Unmanned aerial vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation. ACS Sens 16(1):97. https:\/\/doi.org\/10.3390\/s16010097","journal-title":"ACS Sens"},{"key":"10558_CR46","doi-asserted-by":"publisher","unstructured":"Guo Q, Feng W, Zhou C et al. (2017) Learning dynamic Siamese network for visual object tracking. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 1781\u20131789. https:\/\/doi.org\/10.1109\/ICCV.2017.196","DOI":"10.1109\/ICCV.2017.196"},{"key":"10558_CR47","doi-asserted-by":"publisher","unstructured":"Guo D, Shao Y, Cui Y et al. (2021) Graph attention tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 9538\u20139547. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00942","DOI":"10.1109\/CVPR46437.2021.00942"},{"key":"10558_CR48","doi-asserted-by":"publisher","unstructured":"Guo D, Wang J, Cui Y et al. (2020) SiamCAR: Siamese fully convolutional classification and regression for visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 6268\u20136276. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00630","DOI":"10.1109\/CVPR42600.2020.00630"},{"key":"10558_CR54","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S et al. (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"10558_CR49","doi-asserted-by":"publisher","unstructured":"Hao J, Zhou Y, Zhang G et al. (2018) A review of target tracking algorithm based on UAV. In: Proceedings of the IEEE international conference on cyborg and bionic systems (CBS), pp 328\u2013333. https:\/\/doi.org\/10.1109\/CBS.2018.8612263","DOI":"10.1109\/CBS.2018.8612263"},{"key":"10558_CR52","doi-asserted-by":"publisher","unstructured":"He A, Luo C, Tian X et al. (2018a) A twofold Siamese network for real-time object tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 4834\u20134843. https:\/\/doi.org\/10.1109\/CVPR.2018.00508","DOI":"10.1109\/CVPR.2018.00508"},{"key":"10558_CR53","doi-asserted-by":"publisher","unstructured":"He A, Luo C, Tian X et al. (2018b) Towards a better match in Siamese network based visual object tracker. In: Proceedings of the European conference on computer vision workshops (ECCVW), pp 132\u2013147. https:\/\/doi.org\/10.1007\/978-3-030-11009-3_7","DOI":"10.1007\/978-3-030-11009-3_7"},{"issue":"2","key":"10558_CR50","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","volume":"42","author":"K He","year":"2020","unstructured":"He K, Gkioxari G, Doll\u00e1r P et al. (2020) Mask R-CNN. IEEE Trans Pattern Anal Mach Intell 42(2):386\u2013397. https:\/\/doi.org\/10.1109\/TPAMI.2018.2844175","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10558_CR51","doi-asserted-by":"publisher","unstructured":"Held D, Thrun S, Savarese S (2016) Learning to track at 100 FPS with deep regression networks. In: Proceedings of the European conference on computer vision (ECCV), pp 749\u2013765. https:\/\/doi.org\/10.1007\/978-3-319-46448-0_45","DOI":"10.1007\/978-3-319-46448-0_45"},{"key":"10558_CR55","unstructured":"Howard AG, Zhu M, Chen B et al. (2017) MobileNets: efficient convolutional neural networks for mobile vision applications, pp 1\u20139. arXiv preprint arXiv:1704.04861"},{"key":"10558_CR56","doi-asserted-by":"publisher","unstructured":"Howard A, Sandler M, Chen B et al. (2019) Searching for MobileNetV3. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 1314\u20131324. https:\/\/doi.org\/10.1109\/ICCV.2019.00140","DOI":"10.1109\/ICCV.2019.00140"},{"issue":"8","key":"10558_CR57","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2020","unstructured":"Hu J, Shen L, Albanie S et al. (2020) Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell 42(8):2011\u20132023. https:\/\/doi.org\/10.1109\/TPAMI.2019.2913372","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10558_CR59","doi-asserted-by":"publisher","unstructured":"Huang C, Lucey S, Ramanan D (2017) Learning policies for adaptive tracking with deep feature cascades. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 105\u2013114. https:\/\/doi.org\/10.1109\/ICCV.2017.21","DOI":"10.1109\/ICCV.2017.21"},{"key":"10558_CR58","doi-asserted-by":"publisher","unstructured":"Huang Z, Fu C, Li Y et al. (2019) Learning aberrance repressed correlation filters for real-time UAV tracking. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 2891\u20132900. https:\/\/doi.org\/10.1109\/ICCV.2019.00298","DOI":"10.1109\/ICCV.2019.00298"},{"key":"10558_CR60","doi-asserted-by":"publisher","unstructured":"Huang L, Zhao X, Huang K (2020) GlobalTrack: a simple and strong baseline for long-term tracking. In: Proceedings of the AAAI conference on artificial intelligence (AAAI), pp 11037\u201311044. https:\/\/doi.org\/10.1609\/aaai.v34i07.6758","DOI":"10.1609\/aaai.v34i07.6758"},{"issue":"5","key":"10558_CR61","doi-asserted-by":"publisher","first-page":"6552","DOI":"10.1109\/TPAMI.2022.3212594","volume":"45","author":"S Javed","year":"2022","unstructured":"Javed S, Danelljan M, Khan FS et al. (2022) Visual object tracking with discriminative filters and Siamese networks: a survey and outlook. IEEE Trans Pattern Anal Mach Intell 45(5):6552-6574. https:\/\/doi.org\/10.1109\/TPAMI.2022.3212594","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10558_CR62","doi-asserted-by":"publisher","unstructured":"Jiang B, Luo R, Mao J et al. (2018) Acquisition of localization confidence for accurate object detection. In: Proceedings of the European conference on computer vision (ECCV), pp 784\u2013799. https:\/\/doi.org\/10.1007\/978-3-030-01264-9_48","DOI":"10.1007\/978-3-030-01264-9_48"},{"key":"10558_CR63","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1007\/s10846-018-0954-x","volume":"95","author":"M Karaduman","year":"2019","unstructured":"Karaduman M, C\u0131nar A, Eren H (2019) UAV traffic patrolling via road detection and tracking in anonymous aerial video frames. J Intell Robot Syst 95:675\u2013690. https:\/\/doi.org\/10.1007\/s10846-018-0954-x","journal-title":"J Intell Robot Syst"},{"key":"10558_CR64","unstructured":"Kingma DP, Welling M (2014) Auto-encoding variational Bayes. In: Proceedings of the international conference on learning representations (ICLR), pp 1\u201314"},{"key":"10558_CR65","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of the international conference on learning representations (ICLR), pp 1\u201314"},{"key":"10558_CR66","doi-asserted-by":"publisher","unstructured":"Krebs S, Duraisamy B, Flohr F (2017) A survey on leveraging deep neural networks for object tracking. In: Proceedings of the international conference on intelligent transportation systems (ITSC), pp 411\u2013418. https:\/\/doi.org\/10.1109\/ITSC.2017.8317904","DOI":"10.1109\/ITSC.2017.8317904"},{"key":"10558_CR67","doi-asserted-by":"publisher","unstructured":"Kristan M, Leonardis A, Matas J et al. (2016) The visual object tracking VOT2016 challenge results. In: Proceedings of the European conference on computer vision workshops (ECCVW), pp 777\u2013823. https:\/\/doi.org\/10.1007\/978-3-319-48881-3_54","DOI":"10.1007\/978-3-319-48881-3_54"},{"issue":"6","key":"10558_CR68","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"key":"10558_CR69","unstructured":"Law H, Teng Y, Russakovsky O et al. (2020) CornerNet-Lite: efficient keypoint based object detection. In: Proceedings of the British machine vision conference (BMVC), pp 1\u201315"},{"key":"10558_CR70","doi-asserted-by":"publisher","unstructured":"Leal-Taix\u00e9 L, Canton-Ferrer C, Schindler K (2016) Learning by tracking: Siamese CNN for robust target association. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops (CVPRW), pp 418\u2013425. https:\/\/doi.org\/10.1109\/CVPRW.2016.59","DOI":"10.1109\/CVPRW.2016.59"},{"key":"10558_CR86","doi-asserted-by":"publisher","unstructured":"Li S, Yeung DY (2017) Visual object tracking for unmanned aerial vehicles: a benchmark and new motion models. In: Proceedings of the AAAI conference on artificial intelligence (AAAI), pp 1\u20137. https:\/\/doi.org\/10.1609\/aaai.v31i1.11205","DOI":"10.1609\/aaai.v31i1.11205"},{"issue":"4","key":"10558_CR71","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2508037.2508039","volume":"4","author":"X Li","year":"2013","unstructured":"Li X, Hu W, Shen C et al. (2013) A survey of appearance models in visual object tracking. ACM Trans Intell Syst Technol 4(4):1\u201348. https:\/\/doi.org\/10.1145\/2508037.2508039","journal-title":"ACM Trans Intell Syst Technol"},{"key":"10558_CR82","doi-asserted-by":"publisher","unstructured":"Li Y, Song Y, Luo J (2017) Improving pairwise ranking for multi-label image classification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 1837\u20131845. https:\/\/doi.org\/10.1109\/CVPR.2017.199","DOI":"10.1109\/CVPR.2017.199"},{"key":"10558_CR85","doi-asserted-by":"publisher","unstructured":"Li B, Yan J, Wu W et al. (2018a) High performance visual tracking with Siamese region proposal network. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 8971\u20138980. https:\/\/doi.org\/10.1109\/CVPR.2018.00935","DOI":"10.1109\/CVPR.2018.00935"},{"key":"10558_CR72","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.patcog.2017.11.007","volume":"76","author":"P Li","year":"2018","unstructured":"Li P, Wang D, Wang L et al. (2018b) Deep visual tracking: review and experimental comparison. Pattern Recogn 76:323\u2013338. https:\/\/doi.org\/10.1016\/j.patcog.2017.11.007","journal-title":"Pattern Recogn"},{"key":"10558_CR84","doi-asserted-by":"publisher","unstructured":"Li B, Wu W, Wang Q, et al. (2019a) SiamRPN++: evolution of Siamese visual tracking with very deep networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 4277\u20134286. https:\/\/doi.org\/10.1109\/CVPR.2019.00441","DOI":"10.1109\/CVPR.2019.00441"},{"key":"10558_CR78","doi-asserted-by":"publisher","unstructured":"Li X, Ma C, Wu B et al. (2019b) Target-aware deep tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 1369\u20131378. https:\/\/doi.org\/10.1109\/CVPR.2019.00146","DOI":"10.1109\/CVPR.2019.00146"},{"key":"10558_CR83","doi-asserted-by":"publisher","unstructured":"Li M, Wang YX, Ramanan D (2020a) Towards streaming perception. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 473\u2013488. https:\/\/doi.org\/10.1007\/978-3-030-58536-5_28","DOI":"10.1007\/978-3-030-58536-5_28"},{"key":"10558_CR75","doi-asserted-by":"publisher","unstructured":"Li Y, Fu C, Ding F et al. (2020b) AutoTrack: towards high-performance visual tracking for UAV with automatic spatio-temporal regularization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 11920\u201311929. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01194","DOI":"10.1109\/CVPR42600.2020.01194"},{"key":"10558_CR76","doi-asserted-by":"publisher","unstructured":"Li B, Fu C, Ding F et al. (2021a) ADTrack: target-aware dual filter learning for real-time anti-dark UAV tracking. In: Proceedings of the IEEE international conference on robotics and automation (ICRA), pp 496\u2013502. https:\/\/doi.org\/10.1109\/ICRA48506.2021.9561564","DOI":"10.1109\/ICRA48506.2021.9561564"},{"key":"10558_CR77","unstructured":"Li B, Li Y, Ye J, et al. (2021b) Predictive Visual Tracking: A New Benchmark and Baseline Approach, pp 1\u20138. arXiv preprint arXiv:2103.04508"},{"key":"10558_CR73","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3162892","author":"B Li","year":"2022","unstructured":"Li B, Fu C, Ding F et al. (2022) All-day object tracking for unmanned aerial vehicle. IEEE Trans Mob Comput. https:\/\/doi.org\/10.1109\/TMC.2022.3162892","journal-title":"IEEE Trans Mob Comput"},{"issue":"2","key":"10558_CR74","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1109\/LRA.2023.3236583","volume":"8","author":"S Li","year":"2023","unstructured":"Li S, Fu C, Lu K et al. (2023) Boosting UAV tracking with voxel-based trajectory-aware pre-training. IEEE Robot Autom Lett 8(2):1133\u20131140. https:\/\/doi.org\/10.1109\/LRA.2023.3236583","journal-title":"IEEE Robot Autom Lett"},{"key":"10558_CR79","doi-asserted-by":"publisher","unstructured":"Lin TY, Maire M, Belongie S et al. (2014) Microsoft COCO: common objects in context. In: Proceedings of the European conference on computer vision (ECCV), pp 740\u2013755. https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48","DOI":"10.1007\/978-3-319-10602-1_48"},{"issue":"2","key":"10558_CR80","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"TY Lin","year":"2020","unstructured":"Lin TY, Goyal P, Girshick R et al. (2020) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42(2):318\u2013327. https:\/\/doi.org\/10.1109\/TPAMI.2018.2858826","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"8","key":"10558_CR81","doi-asserted-by":"publisher","first-page":"10469","DOI":"10.1109\/TITS.2021.3094654","volume":"23","author":"F Lin","year":"2021","unstructured":"Lin F, Fu C, He Y et al. (2021) ReCF: exploiting response reasoning for correlation filters in real-time UAV tracking. IEEE Trans Intell Transp Syst 23(8):10469-10480. https:\/\/doi.org\/10.1109\/TITS.2021.3094654","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10558_CR87","doi-asserted-by":"publisher","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 3431\u20133440. https:\/\/doi.org\/10.1109\/CVPR.2015.7298965","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"6","key":"10558_CR88","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/LRA.2023.3264711","volume":"8","author":"K Lu","year":"2023","unstructured":"Lu K, Fu C, Wang Y et al. (2023) Cascaded denoising Transformer for UAV nighttime tracking. IEEE Robot Autom Lett 8(6):3142\u20133149.\u00a0https:\/\/doi.org\/10.1109\/LRA.2023.3264711","journal-title":"IEEE Robot Autom Lett"},{"key":"10558_CR89","doi-asserted-by":"publisher","unstructured":"Luiten J, Voigtlaender P, Leibe B (2018) PReMVOS: proposal-generation, refinement and merging for video object segmentation. In: Proceedings of the Asian conference on computer vision (ACCV), pp 565\u2013580. https:\/\/doi.org\/10.1007\/978-3-030-20870-7_35","DOI":"10.1007\/978-3-030-20870-7_35"},{"key":"10558_CR90","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/s00371-020-01848-y","volume":"56","author":"Y Luo","year":"2022","unstructured":"Luo Y, Yu X, Yang D et al. (2022) A survey of intelligent transmission line inspection based on unmanned aerial vehicle. Artif Intell Rev 56:173-201. https:\/\/doi.org\/10.1007\/s00371-020-01848-y","journal-title":"Artif Intell Rev"},{"key":"10558_CR92","doi-asserted-by":"publisher","unstructured":"Ma N, Zhang X, Zheng HT et al. (2018) ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European conference on computer vision (ECCV), pp 116\u2013131. https:\/\/doi.org\/10.1007\/978-3-030-01264-9_8","DOI":"10.1007\/978-3-030-01264-9_8"},{"issue":"5","key":"10558_CR91","doi-asserted-by":"publisher","first-page":"3943","DOI":"10.1109\/TITS.2020.3046478","volume":"23","author":"SM Marvasti-Zadeh","year":"2022","unstructured":"Marvasti-Zadeh SM, Cheng L, Ghanei-Yakhdan H et al. (2022) Deep learning for visual tracking: a comprehensive survey. IEEE Trans Intell Transp Syst 23(5):3943\u20133968. https:\/\/doi.org\/10.1109\/TITS.2020.3046478","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10558_CR93","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1016\/j.sysarc.2019.01.011","volume":"97","author":"S Mittal","year":"2019","unstructured":"Mittal S (2019) A survey on optimized implementation of deep learning models on the NVIDIA Jetson platform. J Syst Archit 97:428\u2013442. https:\/\/doi.org\/10.1016\/j.sysarc.2019.01.011","journal-title":"J Syst Archit"},{"key":"10558_CR94","doi-asserted-by":"publisher","unstructured":"M\u00fceller M, Smith N, Ghanem B (2016) A benchmark and simulator for Uav tracking. In: Proceedings of the European conference on computer vision (ECCV), pp 445\u2013461. https:\/\/doi.org\/10.1007\/978-3-319-46448-0_27","DOI":"10.1007\/978-3-319-46448-0_27"},{"key":"10558_CR95","doi-asserted-by":"publisher","unstructured":"M\u00fcller M, Bibi A, Giancola S et al. (2018) TrackingNet: a large-scale dataset and benchmark for object tracking in the wild. In: Proceedings of the European conference on computer vision (ECCV), pp 300\u2013317. https:\/\/doi.org\/10.1007\/978-3-030-01246-5_19","DOI":"10.1007\/978-3-030-01246-5_19"},{"issue":"1","key":"10558_CR96","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1109\/TRO.2021.3084395","volume":"38","author":"A Ollero","year":"2021","unstructured":"Ollero A, Tognon M, Suarez A et al. (2021) Past, present, and future of aerial robotic manipulators. IEEE Trans Robot 38(1):626\u2013645. https:\/\/doi.org\/10.1109\/TRO.2021.3084395","journal-title":"IEEE Trans Robot"},{"key":"10558_CR97","doi-asserted-by":"publisher","first-page":"110149","DOI":"10.1109\/ACCESS.2021.3101988","volume":"9","author":"M Ondra\u0161ovi\u010d","year":"2021","unstructured":"Ondra\u0161ovi\u010d M, Tar\u00e1bek P (2021) Siamese visual object tracking: a survey. IEEE Access 9:110149\u2013110172. https:\/\/doi.org\/10.1109\/ACCESS.2021.3101988","journal-title":"IEEE Access"},{"key":"10558_CR98","doi-asserted-by":"publisher","unstructured":"Peng J, Jiang Z, Gu Y et al. (2021) SiamRCR: reciprocal classification and regression for visual object tracking. In: Proceedings of the international joint conference on artificial intelligence (IJCAI), pp 1\u201310. https:\/\/doi.org\/10.24963\/ijcai.2021\/132","DOI":"10.24963\/ijcai.2021\/132"},{"key":"10558_CR99","unstructured":"Pflugfelder R (2017) An in-depth analysis of visual tracking with Siamese neural networks, pp 1\u201319. arXiv preprint arXiv:1707.00569"},{"key":"10558_CR100","doi-asserted-by":"publisher","unstructured":"Real E, Shlens J, Mazzocchi S et al. (2017) YouTube-BoundingBoxes: a large high-precision human-annotated data set for object detection in video. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 7464\u20137473. https:\/\/doi.org\/10.1109\/CVPR.2017.789","DOI":"10.1109\/CVPR.2017.789"},{"issue":"6","key":"10558_CR101","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2015","unstructured":"Ren S, He K, Girshick R et al. (2015) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149. https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10558_CR102","doi-asserted-by":"publisher","unstructured":"Rezatofighi H, Tsoi N, Gwak J et al. (2019) Generalized intersection over union: a metric and a loss for bounding box regression. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 658\u2013666. https:\/\/doi.org\/10.1109\/CVPR.2019.00075","DOI":"10.1109\/CVPR.2019.00075"},{"issue":"3","key":"10558_CR103","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H et al. (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2013252. https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int J Comput Vis"},{"key":"10558_CR104","doi-asserted-by":"publisher","unstructured":"Sandler M, Howard A, Zhu M et al. (2018) MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 4510\u20134520. https:\/\/doi.org\/10.1109\/CVPR.2018.00474","DOI":"10.1109\/CVPR.2018.00474"},{"key":"10558_CR105","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4175.001.0001","volume-title":"Learning with kernels: support vector machines, regularization, optimization, and beyond","author":"B Scholkopf","year":"2001","unstructured":"Scholkopf B, Smola AJ (2001) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge"},{"issue":"4","key":"10558_CR106","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640\u2013651. https:\/\/doi.org\/10.1109\/TPAMI.2016.2572683","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10558_CR107","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proceedings of the international conference on learning representations (ICLR), pp 1\u201314"},{"issue":"7","key":"10558_CR108","doi-asserted-by":"publisher","first-page":"1442","DOI":"10.1109\/TPAMI.2013.230","volume":"36","author":"AW Smeulders","year":"2014","unstructured":"Smeulders AW, Chu DM, Cucchiara R et al. (2014) Visual tracking: an experimental survey. IEEE Trans Pattern Anal Mach Intell 36(7):1442\u20131468. https:\/\/doi.org\/10.1109\/TPAMI.2013.230","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10558_CR109","doi-asserted-by":"publisher","unstructured":"Sosnovik I, Moskalev A, Smeulders A (2021) Scale equivariance improves Siamese tracking. In: Proceedings of the IEEE winter conference on applications of computer vision (WACV), pp 2764\u20132773. https:\/\/doi.org\/10.1109\/WACV48630.2021.00281","DOI":"10.1109\/WACV48630.2021.00281"},{"key":"10558_CR110","unstructured":"Sosnovik I, Szmaja M, Smeulders A (2020) Scale-equivariant steerable networks. In: Proceedings of the international conference on learning representations (ICLR), pp 1\u201314"},{"key":"10558_CR111","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu W, Jia Y et al. (2015) Going deeper with convolutions. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10558_CR112","doi-asserted-by":"publisher","unstructured":"Szegedy C, Vanhoucke V, Ioffe S et al. (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 2818\u20132826. https:\/\/doi.org\/10.1109\/CVPR.2016.308","DOI":"10.1109\/CVPR.2016.308"},{"key":"10558_CR113","doi-asserted-by":"publisher","first-page":"4295","DOI":"10.1007\/s10462-022-10281-7","volume":"56","author":"J Tang","year":"2022","unstructured":"Tang J, Duan H, Lao S (2022) Swarm intelligence algorithms for multiple unmanned aerial vehicles collaboration: a comprehensive review. Artif Intell Rev 56:4295-4327. https:\/\/doi.org\/10.1007\/s10462-022-10281-7","journal-title":"Artif Intell Rev"},{"key":"10558_CR114","doi-asserted-by":"publisher","unstructured":"Tao R, Gavves E, Smeulders AW (2016) Siamese instance search for tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 1420\u20131429. https:\/\/doi.org\/10.1109\/CVPR.2016.158","DOI":"10.1109\/CVPR.2016.158"},{"key":"10558_CR115","doi-asserted-by":"publisher","unstructured":"Tian Z, Shen C, Chen H et al. (2019) FCOS: fully convolutional one-stage object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 9626\u20139635. https:\/\/doi.org\/10.1109\/ICCV.2019.00972","DOI":"10.1109\/ICCV.2019.00972"},{"key":"10558_CR116","doi-asserted-by":"publisher","unstructured":"Tony LA, Jana S, Varun V, et al. (2022) UAV collaboration for autonomous target capture. In: Proceedings of the congress on intelligent systems (CIS), pp 847\u2013862. https:\/\/doi.org\/10.1007\/978-981-16-9416-5_62","DOI":"10.1007\/978-981-16-9416-5_62"},{"issue":"2","key":"10558_CR117","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","volume":"104","author":"JR Uijlings","year":"2013","unstructured":"Uijlings JR, Van De Sande KE, Gevers T et al. (2013) Selective search for object recognition. Int J Comput Vis 104(2):154\u2013171. https:\/\/doi.org\/10.1007\/s11263-013-0620-5","journal-title":"Int J Comput Vis"},{"key":"10558_CR118","doi-asserted-by":"publisher","unstructured":"Valmadre J, Bertinetto L, Henriques J et al. (2017) End-to-end representation learning for correlation filter based tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 5000\u20135008. https:\/\/doi.org\/10.1109\/CVPR.2017.531","DOI":"10.1109\/CVPR.2017.531"},{"key":"10558_CR119","unstructured":"Vaswani A, Shazeer N, Parmar N et al. (2017) Attention is all you need. In: Proceedings of the advances in neural information processing systems (NeurIPS), pp 1\u201311"},{"key":"10558_CR120","doi-asserted-by":"publisher","unstructured":"Vedaldi A, Lenc K (2015) MatConvNet: convolutional neural networks for MATLAB. In: Proceedings of the ACM multimedia conference (MM), pp 689\u2013692. https:\/\/doi.org\/10.1145\/2733373.2807412","DOI":"10.1145\/2733373.2807412"},{"key":"10558_CR121","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A et al. (2018) Graph attention networks. In: Proceedings of the international conference on learning representations (ICLR), pp 1\u201312"},{"key":"10558_CR122","doi-asserted-by":"publisher","unstructured":"Voigtlaender P, Luiten J, Torr PH et al. (2020) Siam R-CNN: visual tracking by re-detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 6577\u20136587. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00661","DOI":"10.1109\/CVPR42600.2020.00661"},{"key":"10558_CR124","doi-asserted-by":"publisher","unstructured":"Wang Q, Teng Z, Xing J et al. (2018a) Learning attentions: residual attentional Siamese network for high performance online visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 4854\u20134863. https:\/\/doi.org\/10.1109\/CVPR.2018.00510","DOI":"10.1109\/CVPR.2018.00510"},{"key":"10558_CR123","doi-asserted-by":"publisher","unstructured":"Wang X, Li C, Luo B et al. (2018b) SINT++: robust visual tracking via adversarial positive instance generation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 4864\u20134873. https:\/\/doi.org\/10.1109\/CVPR.2018.00511","DOI":"10.1109\/CVPR.2018.00511"},{"key":"10558_CR126","doi-asserted-by":"publisher","unstructured":"Wang Q, Zhang L, Bertinetto L et al. (2019) Fast online object tracking and segmentation: a unifying approach. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 1328\u20131338. https:\/\/doi.org\/10.1109\/CVPR.2019.00142","DOI":"10.1109\/CVPR.2019.00142"},{"key":"10558_CR127","doi-asserted-by":"publisher","unstructured":"Wang H, Zhu Y, Adam H et al. (2021a) Max-Deeplab: end-to-end panoptic segmentation with mask Transformers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 5459\u20135470. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00542","DOI":"10.1109\/CVPR46437.2021.00542"},{"key":"10558_CR125","doi-asserted-by":"publisher","unstructured":"Wang Y, Xu Z, Wang X et al. (2021b) End-to-end video instance segmentation with Transformers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 8737\u20138746. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00863","DOI":"10.1109\/CVPR46437.2021.00863"},{"issue":"9","key":"10558_CR128","doi-asserted-by":"publisher","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","volume":"37","author":"Y Wu","year":"2015","unstructured":"Wu Y, Lim J, Yang MH (2015) Object tracking benchmark. IEEE Trans Pattern Anal Mach Intell 37(9):1834\u20131848. https:\/\/doi.org\/10.1109\/TPAMI.2014.2388226","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"10558_CR129","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1109\/MGRS.2021.3115137","volume":"10","author":"X Wu","year":"2022","unstructured":"Wu X, Li W, Hong D et al. (2022) Deep learning for unmanned aerial vehicle-based object detection and tracking: a survey. IEEE Geosci Remote Sens Mag 10(1):91\u2013124. https:\/\/doi.org\/10.1109\/MGRS.2021.3115137","journal-title":"IEEE Geosci Remote Sens Mag"},{"key":"10558_CR130","doi-asserted-by":"publisher","unstructured":"Xie S, Girshick R, Doll\u00e1r P et al. (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 5987\u20135995. https:\/\/doi.org\/10.1109\/CVPR.2017.634","DOI":"10.1109\/CVPR.2017.634"},{"key":"10558_CR131","doi-asserted-by":"publisher","unstructured":"Xu Y, Wang Z, Li Z et al. (2020) SiamFC++: towards robust and accurate visual tracking with target estimation guidelines. In: Proceedings of the AAAI conference on artificial intelligence (AAAI), pp 12549\u201312556. https:\/\/doi.org\/10.1609\/aaai.v34i07.6944","DOI":"10.1609\/aaai.v34i07.6944"},{"key":"10558_CR136","doi-asserted-by":"publisher","unstructured":"Yan B, Wang D, Lu H et al. (2020) Cooling-shrinking attack: blinding the tracker with imperceptible noises. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 987\u2013996. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00107","DOI":"10.1109\/CVPR42600.2020.00107"},{"key":"10558_CR134","doi-asserted-by":"publisher","unstructured":"Yan B, Peng H, Fu J et al. (2021a) Learning spatio-temporal Transformer for visual tracking. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 10428\u201310437. https:\/\/doi.org\/10.1109\/ICCV48922.2021.01028","DOI":"10.1109\/ICCV48922.2021.01028"},{"key":"10558_CR135","doi-asserted-by":"publisher","unstructured":"Yan B, Peng H, Wu K et al. (2021b) LightTrack: finding lightweight neural networks for object tracking via one-shot architecture search. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 15175\u201315184. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01493","DOI":"10.1109\/CVPR46437.2021.01493"},{"issue":"18","key":"10558_CR132","doi-asserted-by":"publisher","first-page":"3823","DOI":"10.1016\/j.neucom.2011.07.024","volume":"74","author":"H Yang","year":"2011","unstructured":"Yang H, Shao L, Zheng F et al. (2011) Recent advances and trends in visual tracking: a review. Neurocomputing 74(18):3823\u20133831. https:\/\/doi.org\/10.1016\/j.neucom.2011.07.024","journal-title":"Neurocomputing"},{"key":"10558_CR133","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.1109\/TMM.2021.3074239","volume":"24","author":"K Yang","year":"2022","unstructured":"Yang K, He Z, Pei W et al. (2022) SiamCorners: Siamese corner networks for visual tracking. IEEE Trans Multimed 24:1956\u20131967. https:\/\/doi.org\/10.1109\/TMM.2021.3074239","journal-title":"IEEE Trans Multimed"},{"key":"10558_CR137","doi-asserted-by":"crossref","unstructured":"Yao L, Fu C, Li S et al. (2023) SGDViT: saliency-guided dynamic vision Transformer for UAV tracking, In: Proceedings of the IEEE international conference on robotics and automation (ICRA), pp 3353-3359. https:\/\/doi.org\/10.1109\/ICRA48891.2023.10161487","DOI":"10.1109\/ICRA48891.2023.10161487"},{"key":"10558_CR140","doi-asserted-by":"publisher","unstructured":"Ye J, Fu C, Zheng G et al. (2021) DarkLighter: light up the darkness for UAV tracking. In: Proceedings of the IEEE\/RSJ international conference on intelligent robots and systems (IROS), pp 3079\u20133085. https:\/\/doi.org\/10.1109\/IROS51168.2021.9636680","DOI":"10.1109\/IROS51168.2021.9636680"},{"issue":"2","key":"10558_CR138","doi-asserted-by":"publisher","first-page":"3866","DOI":"10.1109\/LRA.2022.3146911","volume":"7","author":"J Ye","year":"2022","unstructured":"Ye J, Fu C, Cao Z et al. (2022a) Tracker meets night: a Transformer enhancer for UAV tracking. IEEE Robot Autom Lett 7(2):3866\u20133873. https:\/\/doi.org\/10.1109\/LRA.2022.3146911","journal-title":"IEEE Robot Autom Lett"},{"issue":"6","key":"10558_CR139","doi-asserted-by":"publisher","first-page":"6004","DOI":"10.1109\/TIE.2021.3088366","volume":"69","author":"J Ye","year":"2022","unstructured":"Ye J, Fu C, Lin F et al. (2022b) Multi-regularized correlation filter for UAV tracking and self-localization. IEEE Trans Ind Electron 69(6):6004\u20136014. https:\/\/doi.org\/10.1109\/TIE.2021.3088366","journal-title":"IEEE Trans Ind Electron"},{"key":"10558_CR141","doi-asserted-by":"publisher","unstructured":"Ye J, Fu C, Zheng G et al. (2022c) Unsupervised domain adaptation for nighttime aerial tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 8886\u20138895. https:\/\/doi.org\/10.1109\/CVPR52688.2022.00869","DOI":"10.1109\/CVPR52688.2022.00869"},{"issue":"4","key":"10558_CR142","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1145\/1177352.1177355","volume":"38","author":"A Yilmaz","year":"2006","unstructured":"Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):13\u201345. https:\/\/doi.org\/10.1145\/1177352.1177355","journal-title":"ACM Comput Surv"},{"key":"10558_CR143","unstructured":"You S, Zhu H, Li M et al. (2019) A review of visual trackers and analysis of its application to mobile robot, pp 1\u201325. arXiv preprint arXiv:1910.09761"},{"key":"10558_CR144","doi-asserted-by":"publisher","unstructured":"Yu J, Jiang Y, Wang Z et al. (2016) UnitBox: an advanced object detection network. In: Proceedings of the ACM multimedia conference (MM), pp 516\u2013520. https:\/\/doi.org\/10.1145\/2964284.2967274","DOI":"10.1145\/2964284.2967274"},{"key":"10558_CR145","doi-asserted-by":"publisher","unstructured":"Yu Y, Xiong Y, Huang W et al. (2020) Deformable Siamese attention networks for visual object tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 6727\u20136736. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00676","DOI":"10.1109\/CVPR42600.2020.00676"},{"key":"10558_CR146","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.cviu.2017.10.007","volume":"164","author":"S Zagoruyko","year":"2017","unstructured":"Zagoruyko S, Komodakis N (2017) Deep compare: a study on using convolutional neural networks to compare image patches. Comput Vis Image Underst 164:38\u201355. https:\/\/doi.org\/10.1016\/j.cviu.2017.10.007","journal-title":"Comput Vis Image Underst"},{"key":"10558_CR147","doi-asserted-by":"publisher","unstructured":"Zhang H, Dana K, Shi J et al. (2018a) Context encoding for semantic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 7151\u20137160. https:\/\/doi.org\/10.1109\/CVPR.2018.00747","DOI":"10.1109\/CVPR.2018.00747"},{"key":"10558_CR152","doi-asserted-by":"publisher","unstructured":"Zhang X, Zhou X, Lin M et al. (2018b) ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 6848\u20136856. https:\/\/doi.org\/10.1109\/CVPR.2018.00716","DOI":"10.1109\/CVPR.2018.00716"},{"key":"10558_CR151","doi-asserted-by":"publisher","unstructured":"Zhang Y, Wang L, Qi J et al. (2018c) Structured Siamese network for real-time visual tracking. In: Proceedings of the European conference on computer vision (ECCV), pp 351\u2013366. https:\/\/doi.org\/10.1007\/978-3-030-01240-3_22","DOI":"10.1007\/978-3-030-01240-3_22"},{"key":"10558_CR149","doi-asserted-by":"publisher","unstructured":"Zhang Z, Peng H (2019) Deeper and wider Siamese networks for real-time visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 4586\u20134595. https:\/\/doi.org\/10.1109\/CVPR.2019.00472","DOI":"10.1109\/CVPR.2019.00472"},{"key":"10558_CR148","doi-asserted-by":"publisher","unstructured":"Zhang L, Gonzalez-Garcia A, Weijer JVD et al. (2019) Learning the model update for Siamese trackers. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 4009\u20134018. https:\/\/doi.org\/10.1109\/ICCV.2019.00411","DOI":"10.1109\/ICCV.2019.00411"},{"key":"10558_CR150","doi-asserted-by":"publisher","unstructured":"Zhang Z, Peng H, Fu J et al. (2020) Ocean: object-aware anchor-free tracking. In: Proceedings of the European conference on computer vision (ECCV), pp 771\u2013787. https:\/\/doi.org\/10.1007\/978-3-030-58589-1_46","DOI":"10.1007\/978-3-030-58589-1_46"},{"key":"10558_CR153","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3228197","author":"G Zheng","year":"2022","unstructured":"Zheng G, Fu C, Ye J et al. (2022a) Scale-aware Siamese object tracking for vision-based UAM approaching. IEEE Trans Ind Inform pp 1-12. https:\/\/doi.org\/10.1109\/TII.2022.3228197","journal-title":"IEEE Trans Ind Inform"},{"key":"10558_CR154","doi-asserted-by":"publisher","unstructured":"Zheng G, Fu C, Ye J et al. (2022b) Siamese object tracking for vision-based UAM approaching with pairwise scale-channel attention. In: Proceedings of the IEEE\/RSJ international conference on intelligent robots and systems (IROS), pp 10486\u201310492. https:\/\/doi.org\/10.1109\/IROS47612.2022.9982189","DOI":"10.1109\/IROS47612.2022.9982189"},{"key":"10558_CR155","doi-asserted-by":"publisher","first-page":"3597","DOI":"10.1109\/TIP.2021.3060905","volume":"30","author":"W Zhou","year":"2021","unstructured":"Zhou W, Wen L, Zhang L et al. (2021) SiamCAN: real-time visual tracking based on Siamese center-aware network. IEEE Trans Image Process 30:3597\u20133609. https:\/\/doi.org\/10.1109\/TIP.2021.3060905","journal-title":"IEEE Trans Image Process"},{"key":"10558_CR156","doi-asserted-by":"publisher","unstructured":"Zhu Z, Wang Q, Li B, et al. (2018) Distractor-aware Siamese networks for visual object tracking. In: Proceedings of the European conference on computer vision (ECCV), pp 101\u2013117. https:\/\/doi.org\/10.1007\/978-3-030-01240-3_7","DOI":"10.1007\/978-3-030-01240-3_7"},{"issue":"2","key":"10558_CR157","doi-asserted-by":"publisher","first-page":"1101","DOI":"10.1109\/LRA.2023.3236584","volume":"8","author":"H Zuo","year":"2023","unstructured":"Zuo H, Fu C, Li S et al. (2023) Adversarial blur-deblur network for robust UAV tracking. IEEE Robot Autom Lett 8(2):1101\u20131108. https:\/\/doi.org\/10.1109\/LRA.2023.3236584","journal-title":"IEEE Robot Autom Lett"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10558-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-023-10558-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10558-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T04:28:09Z","timestamp":1729830489000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-023-10558-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,27]]},"references-count":157,"journal-issue":{"issue":"S1","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["10558"],"URL":"https:\/\/doi.org\/10.1007\/s10462-023-10558-5","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,27]]},"assertion":[{"value":"27 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Confict of interest"}}]}}