{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T19:21:42Z","timestamp":1778354502482,"version":"3.51.4"},"reference-count":59,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T00:00:00Z","timestamp":1748390400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014434","name":"National Agency for Academic Exchange","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014434","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006356","name":"Syddansk Universitet","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006356","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004239","name":"Politechnika Pozna\u0144ska","doi-asserted-by":"publisher","award":["2022\\u20132024"],"award-info":[{"award-number":["2022\\u20132024"]}],"id":[{"id":"10.13039\/501100004239","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1016\/j.neucom.2025.130503","type":"journal-article","created":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T11:12:14Z","timestamp":1748430734000},"page":"130503","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Improving consistency of marine mammals tracking in challenging drone recordings through visual particle filter integration"],"prefix":"10.1016","volume":"646","author":[{"given":"Bartosz","family":"Ptak","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3310-5680","authenticated-orcid":false,"given":"Henrik Skov","family":"Midtiby","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6483-2357","authenticated-orcid":false,"given":"Marek","family":"Kraft","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2025.130503_b1","doi-asserted-by":"crossref","first-page":"91","DOI":"10.3354\/esr00958","article-title":"Importance of machine learning for enhancing ecological studies using information-rich imagery","volume":"39","author":"Dujon","year":"2019","journal-title":"Endanger. Species Res."},{"issue":"1","key":"10.1016\/j.neucom.2025.130503_b2","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3390\/rs14010021","article-title":"Feasibility of using small UAVs to derive morphometric measurements of Australian snubfin (Orcaella heinsohni) and humpback (Sousa sahulensis) dolphins","volume":"14","author":"Christie","year":"2021","journal-title":"Remote. Sens."},{"issue":"3","key":"10.1016\/j.neucom.2025.130503_b3","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1002\/aqc.3440","article-title":"Responses of bottlenose dolphins (Tursiops spp.) to small drones","volume":"31","author":"Giles","year":"2021","journal-title":"Aquat. Conserv.: Mar. Freshw. Ecosyst."},{"issue":"1","key":"10.1016\/j.neucom.2025.130503_b4","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-43453-8","article-title":"Wild harbour porpoises startle and flee at low received levels from acoustic harassment device","volume":"13","author":"Elmegaard","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.neucom.2025.130503_b5","series-title":"Cooperation-based concept formation in male bottlenose dolphins","author":"King","year":"2021"},{"issue":"7553","key":"10.1016\/j.neucom.2025.130503_b6","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.neucom.2025.130503_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.artint.2020.103448","article-title":"Multiple object tracking: A literature review","volume":"293","author":"Luo","year":"2021","journal-title":"Artificial Intelligence"},{"key":"10.1016\/j.neucom.2025.130503_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109161","article-title":"Deep learning in multiple animal tracking: A survey","volume":"224","author":"Liu","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.neucom.2025.130503_b9","doi-asserted-by":"crossref","unstructured":"C. Feichtenhofer, A. Pinz, A. Zisserman, Detect to track and track to detect, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 3038\u20133046.","DOI":"10.1109\/ICCV.2017.330"},{"key":"10.1016\/j.neucom.2025.130503_b10","first-page":"215","article-title":"A review on object detection in unmanned aerial vehicle surveillance","volume":"2","author":"Ramachandran","year":"2021","journal-title":"Int. J. Cogn. Comput. Eng."},{"key":"10.1016\/j.neucom.2025.130503_b11","article-title":"Development and challenges of object detection: A survey","author":"Li","year":"2024","journal-title":"Neurocomputing"},{"issue":"15","key":"10.1016\/j.neucom.2025.130503_b12","doi-asserted-by":"crossref","first-page":"6887","DOI":"10.3390\/s23156887","article-title":"Small object detection and tracking: A comprehensive review","volume":"23","author":"Mirzaei","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.neucom.2025.130503_b13","unstructured":"B. Kiefer, M. Kristan, J. Per\u0161, L. \u017dust, F. Poiesi, F. Andrade, A. Bernardino, M. Dawkins, J. Raitoharju, Y. Quan, et al., 1st workshop on maritime computer vision (MACVI) 2023: Challenge results, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2023, pp. 265\u2013302."},{"key":"10.1016\/j.neucom.2025.130503_b14","series-title":"2016 IEEE International Conference on Image Processing","first-page":"3464","article-title":"Simple online and realtime tracking","author":"Bewley","year":"2016"},{"key":"10.1016\/j.neucom.2025.130503_b15","series-title":"2017 IEEE International Conference on Image Processing","first-page":"3645","article-title":"Simple online and realtime tracking with a deep association metric","author":"Wojke","year":"2017"},{"key":"10.1016\/j.neucom.2025.130503_b16","doi-asserted-by":"crossref","unstructured":"B. Shuai, A. Berneshawi, X. Li, D. Modolo, J. Tighe, Siammot: Siamese multi-object tracking, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 12372\u201312382.","DOI":"10.1109\/CVPR46437.2021.01219"},{"key":"10.1016\/j.neucom.2025.130503_b17","doi-asserted-by":"crossref","unstructured":"H. Ren, S. Han, H. Ding, Z. Zhang, H. Wang, F. Wang, Focus on details: Online multi-object tracking with diverse fine-grained representation, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 11289\u201311298.","DOI":"10.1109\/CVPR52729.2023.01086"},{"key":"10.1016\/j.neucom.2025.130503_b18","unstructured":"N. Aharon, R. Orfaig, B. Bobrovsky, BoT-SORT: Robust associations multi-pedestrian tracking, arXiv preprint arXiv:2206.14651."},{"key":"10.1016\/j.neucom.2025.130503_b19","doi-asserted-by":"crossref","unstructured":"J. Seidenschwarz, G. Bras\u00f3, V.C. Serrano, I. Elezi, L. Leal-Taix\u00e9, Simple cues lead to a strong multi-object tracker, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 13813\u201313823.","DOI":"10.1109\/CVPR52729.2023.01327"},{"key":"10.1016\/j.neucom.2025.130503_b20","doi-asserted-by":"crossref","unstructured":"D. Levy, Y. Belfer, E. Osherov, E. Bigal, A.P. Scheinin, H. Nativ, D. Tchernov, T. Treibitz, Automated analysis of marine video with limited data, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018, pp. 1385\u20131393.","DOI":"10.1109\/CVPRW.2018.00187"},{"key":"10.1016\/j.neucom.2025.130503_b21","doi-asserted-by":"crossref","unstructured":"T.-Y. Lin, P. Goyal, R. Girshick, K. He, P. Doll\u00e1r, Focal loss for dense object detection, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"10.1016\/j.neucom.2025.130503_b22","doi-asserted-by":"crossref","unstructured":"J. Redmon, A. Farhadi, YOLO9000: Better, faster, stronger, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 7263\u20137271.","DOI":"10.1109\/CVPR.2017.690"},{"issue":"3","key":"10.1016\/j.neucom.2025.130503_b23","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1002\/rse2.205","article-title":"Machine learning to detect marine animals in UAV imagery: Effect of morphology, spacing, behaviour and habitat","volume":"7","author":"Dujon","year":"2021","journal-title":"Remote. Sens. Ecol. Conserv."},{"key":"10.1016\/j.neucom.2025.130503_b24","doi-asserted-by":"crossref","DOI":"10.3389\/fmars.2022.981897","article-title":"Assessing the ability of deep learning techniques to perform real-time identification of shark species in live streaming video from drones","volume":"9","author":"Purcell","year":"2022","journal-title":"Front. Mar. Sci."},{"issue":"22","key":"10.1016\/j.neucom.2025.130503_b25","doi-asserted-by":"crossref","first-page":"9193","DOI":"10.3390\/s23229193","article-title":"Utility of spectral filtering to improve the reliability of marine fauna detections from drone-based monitoring","volume":"23","author":"Colefax","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.neucom.2025.130503_b26","doi-asserted-by":"crossref","first-page":"8725","DOI":"10.1109\/TMM.2023.3240881","article-title":"StrongSORT: Make deepsort great again","volume":"25","author":"Du","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.neucom.2025.130503_b27","unstructured":"J. Cao, X. Weng, R. Khirodkar, J. Pang, K. Kitani, Observation-centric SORT: Rethinking sort for robust multi-object tracking, arXiv preprint arXiv:2203.14360."},{"issue":"10","key":"10.1016\/j.neucom.2025.130503_b28","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1109\/TPAMI.2008.113","article-title":"Parametric image alignment using enhanced correlation coefficient maximization","volume":"30","author":"Evangelidis","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1\u201310","key":"10.1016\/j.neucom.2025.130503_b29","first-page":"4","article-title":"Pyramidal implementation of the affine Lucas Kanade feature tracker description of the algorithm","volume":"5","author":"Bouguet","year":"2001","journal-title":"Intel Corp."},{"key":"10.1016\/j.neucom.2025.130503_b30","series-title":"2011 International Conference on Computer Vision","first-page":"2564","article-title":"ORB: An efficient alternative to SIFT or SURF","author":"Rublee","year":"2011"},{"issue":"5","key":"10.1016\/j.neucom.2025.130503_b31","doi-asserted-by":"crossref","first-page":"3943","DOI":"10.1109\/TITS.2020.3046478","article-title":"Deep learning for visual tracking: A comprehensive survey","volume":"23","author":"Marvasti-Zadeh","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2025.130503_b32","series-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"240","article-title":"Discriminatively trained particle filters for complex multi-object tracking","author":"Hess","year":"2009"},{"issue":"1","key":"10.1016\/j.neucom.2025.130503_b33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11042-010-0676-y","article-title":"Tracking video objects with feature points based particle filtering","volume":"58","author":"Gao","year":"2012","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.neucom.2025.130503_b34","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.neucom.2025.130503_b35","series-title":"2017 IEEE International Conference on Image Processing","first-page":"3650","article-title":"Deep convolutional particle filter for visual tracking","author":"Mozhdehi","year":"2017"},{"key":"10.1016\/j.neucom.2025.130503_b36","article-title":"Deep convolutional correlation iterative particle filter for visual tracking","volume":"222","author":"Mozhdehi","year":"2022","journal-title":"Comput. Vis. Image Underst."},{"key":"10.1016\/j.neucom.2025.130503_b37","doi-asserted-by":"crossref","unstructured":"R. Sundararaman, C. De Almeida Braga, E. Marchand, J. Pettre, Tracking pedestrian heads in dense crowd, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 3865\u20133875.","DOI":"10.1109\/CVPR46437.2021.00386"},{"key":"10.1016\/j.neucom.2025.130503_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2022.101794","article-title":"Instance segmentation and tracking of animals in wildlife videos: SWIFT-segmentation with filtering of tracklets","volume":"71","author":"Schindler","year":"2022","journal-title":"Ecol. Informatics"},{"key":"10.1016\/j.neucom.2025.130503_b39","series-title":"European Conference on Computer Vision","first-page":"1","article-title":"Bytetrack: Multi-object tracking by associating every detection box","author":"Zhang","year":"2022"},{"key":"10.1016\/j.neucom.2025.130503_b40","article-title":"Gaussian processes for regression","volume":"8","author":"Williams","year":"1995","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2025.130503_b41","doi-asserted-by":"crossref","unstructured":"F. Saleh, S. Aliakbarian, H. Rezatofighi, M. Salzmann, S. Gould, Probabilistic tracklet scoring and inpainting for multiple object tracking, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 14329\u201314339.","DOI":"10.1109\/CVPR46437.2021.01410"},{"key":"10.1016\/j.neucom.2025.130503_b42","doi-asserted-by":"crossref","unstructured":"Z. Qin, S. Zhou, L. Wang, J. Duan, G. Hua, W. Tang, Motiontrack: Learning robust short-term and long-term motions for multi-object tracking, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 17939\u201317948.","DOI":"10.1109\/CVPR52729.2023.01720"},{"issue":"35","key":"10.1016\/j.neucom.2025.130503_b43","doi-asserted-by":"crossref","first-page":"26023","DOI":"10.1007\/s11042-020-09242-5","article-title":"Efficient vehicle detection and tracking strategy in aerial videos by employing morphological operations and feature points motion analysis","volume":"79","author":"Gomaa","year":"2020","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.neucom.2025.130503_b44","series-title":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","first-page":"593","article-title":"Good features to track","author":"Shi","year":"1994"},{"key":"10.1016\/j.neucom.2025.130503_b45","series-title":"Mesh-SORT: Simple and effective location-wise tracker with lost management strategies","author":"Li","year":"2023"},{"key":"10.1016\/j.neucom.2025.130503_b46","series-title":"Lost and found: Overcoming detector failures in online multi-object tracking","author":"Vaquero","year":"2024"},{"key":"10.1016\/j.neucom.2025.130503_b47","series-title":"YOLOv10 to its genesis: A decadal and comprehensive review of the you only look once (YOLO) series","author":"Sapkota","year":"2024"},{"issue":"3","key":"10.1016\/j.neucom.2025.130503_b48","doi-asserted-by":"crossref","first-page":"190","DOI":"10.3390\/drones7030190","article-title":"YOLO-based UAV technology: A review of the research and its applications","volume":"7","author":"Chen","year":"2023","journal-title":"Drones"},{"key":"10.1016\/j.neucom.2025.130503_b49","series-title":"YOLOv9: Learning what you want to learn using programmable gradient information","author":"Wang","year":"2024"},{"key":"10.1016\/j.neucom.2025.130503_b50","series-title":"International Conference on Machine Learning","first-page":"1139","article-title":"On the importance of initialization and momentum in deep learning","author":"Sutskever","year":"2013"},{"key":"10.1016\/j.neucom.2025.130503_b51","doi-asserted-by":"crossref","unstructured":"C.-Y. Wang, A. Bochkovskiy, H.-Y.M. Liao, YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 7464\u20137475.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"10.1016\/j.neucom.2025.130503_b52","first-page":"29935","article-title":"Data augmentation can improve robustness","volume":"34","author":"Rebuffi","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2025.130503_b53","doi-asserted-by":"crossref","unstructured":"G. Ghiasi, Y. Cui, A. Srinivas, R. Qian, T.-Y. Lin, E.D. Cubuk, Q.V. Le, B. Zoph, Simple copy-paste is a strong data augmentation method for instance segmentation, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 2918\u20132928.","DOI":"10.1109\/CVPR46437.2021.00294"},{"key":"10.1016\/j.neucom.2025.130503_b54","series-title":"Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6\u201312, 2014, Proceedings, Part V 13","first-page":"740","article-title":"Microsoft COCO: Common objects in context","author":"Lin","year":"2014"},{"key":"10.1016\/j.neucom.2025.130503_b55","series-title":"2008 First Workshops on Image Processing Theory, Tools and Applications","first-page":"1","article-title":"The local binary pattern approach and its applications to face analysis","author":"Hadid","year":"2008"},{"issue":"6","key":"10.1016\/j.neucom.2025.130503_b56","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1016\/S1874-1029(13)60051-8","article-title":"Research and perspective on local binary pattern","volume":"39","author":"Ke-Chen","year":"2013","journal-title":"Acta Automat. Sinica"},{"key":"10.1016\/j.neucom.2025.130503_b57","series-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2953","article-title":"Learning to associate: HybridBoosted multi-target tracker for crowded scene","author":"Li","year":"2009"},{"key":"10.1016\/j.neucom.2025.130503_b58","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1007\/s11263-020-01375-2","article-title":"HOTA: A higher order metric for evaluating multi-object tracking","volume":"129","author":"Luiten","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.neucom.2025.130503_b59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2008\/246309","article-title":"Evaluating multiple object tracking performance: The clear MOT metrics","volume":"2008","author":"Bernardin","year":"2008","journal-title":"EURASIP J. Image Video Process."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231225011750?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231225011750?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T05:53:49Z","timestamp":1762322029000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231225011750"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":59,"alternative-id":["S0925231225011750"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2025.130503","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Improving consistency of marine mammals tracking in challenging drone recordings through visual particle filter integration","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2025.130503","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"130503"}}