{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T18:10:15Z","timestamp":1743012615922,"version":"3.37.3"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,4,4]],"date-time":"2021-04-04T00:00:00Z","timestamp":1617494400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,4]],"date-time":"2021-04-04T00:00:00Z","timestamp":1617494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"crossref","award":["61871106"],"award-info":[{"award-number":["61871106"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s11063-021-10507-9","type":"journal-article","created":{"date-parts":[[2021,4,4]],"date-time":"2021-04-04T06:02:38Z","timestamp":1617516158000},"page":"2305-2329","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Wasserstein Distance-Based Auto-Encoder Tracking"],"prefix":"10.1007","volume":"53","author":[{"given":"Long","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0915-5378","authenticated-orcid":false,"given":"Ying","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenhe","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuaqiao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaofu","family":"Diao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,4]]},"reference":[{"key":"10507_CR1","doi-asserted-by":"crossref","unstructured":"Bertinetto L, Valmadre J, Henriques J F, et al (2016) Fully-convolutional siamese networks for object tracking[C]. In: European conference on computer vision. Springer, Cham, pp 850\u2013865","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"10507_CR2","doi-asserted-by":"crossref","unstructured":"Danelljan M, Hager G, Shahbaz Khan F, et al (2015) Learning spatially regularized correlation filters for visual tracking[C]. In: Proceedings of the IEEE international conference on computer vision, pp 4310\u20134318","DOI":"10.1109\/ICCV.2015.490"},{"key":"10507_CR3","doi-asserted-by":"crossref","unstructured":"Ma C, Huang J B, Yang X, et al (2015) Hierarchical convolutional features for visual tracking[C]. In: Proceedings of the IEEE international conference on computer vision, pp 3074\u20133082","DOI":"10.1109\/ICCV.2015.352"},{"key":"10507_CR4","unstructured":"Hong S, You T, Kwak S, et al (2015) Online tracking by learning discriminative saliency map with convolutional neural network[C]. In: International conference on machine learning, pp 597\u2013606"},{"key":"10507_CR5","doi-asserted-by":"publisher","first-page":"75244","DOI":"10.1109\/ACCESS.2018.2883650","volume":"6","author":"MM Islam","year":"2018","unstructured":"Islam MM, Hu G, Liu Q et al (2018) Correlation filter based moving object tracking with scale adaptation and online re-detection[J]. IEEE Access 6:75244\u201375258","journal-title":"IEEE Access"},{"key":"10507_CR6","doi-asserted-by":"crossref","unstructured":"Wu Y, Lim J, Yang MH (2013) Online Object Tracking: A Benchmark[C]. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society","DOI":"10.1109\/CVPR.2013.312"},{"issue":"9","key":"10507_CR7","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[J]. IEEE Transac Pattern Analy Mach Intell 37(9):1834\u20131848","journal-title":"IEEE Transac Pattern Analy Mach Intell"},{"issue":"12","key":"10507_CR8","doi-asserted-by":"publisher","first-page":"5630","DOI":"10.1109\/TIP.2015.2482905","volume":"24","author":"P Liang","year":"2015","unstructured":"Liang P, Blasch E, Ling H (2015) Encoding color information for visual tracking: algorithms and benchmark[J]. IEEE Transac Image Process 24(12):5630\u20135644","journal-title":"IEEE Transac Image Process"},{"key":"10507_CR9","doi-asserted-by":"crossref","unstructured":"Li B, Yan J, Wu W, et al (2018) High performance visual tracking with siamese region proposal network[C]. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 8971\u20138980","DOI":"10.1109\/CVPR.2018.00935"},{"key":"10507_CR10","doi-asserted-by":"crossref","unstructured":"Li B, Wu W, Wang Q, et al (2019) Siamrpn++: Evolution of siamese visual tracking with very deep networks[C]. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4282\u20134291","DOI":"10.1109\/CVPR.2019.00441"},{"key":"10507_CR11","doi-asserted-by":"crossref","unstructured":"Nam H, Han B (2016) Learning multi-domain convolutional neural networks for visual tracking[C]. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4293\u20134302","DOI":"10.1109\/CVPR.2016.465"},{"key":"10507_CR12","doi-asserted-by":"crossref","unstructured":"Danelljan M, Bhat G, Khan F S, et al (2019) Atom: Accurate tracking by overlap maximization[C]. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4660\u20134669","DOI":"10.1109\/CVPR.2019.00479"},{"key":"10507_CR13","doi-asserted-by":"crossref","unstructured":"Zhang J, Ma S, Sclaroff S (2014) MEEM: robust tracking via multiple experts using entropy minimization[C]. In: European conference on computer vision. Springer, Cham, pp 188\u2013203","DOI":"10.1007\/978-3-319-10599-4_13"},{"key":"10507_CR14","doi-asserted-by":"crossref","unstructured":"Bolme D S, Beveridge J R, Draper B A, et al (2010) Visual object tracking using adaptive correlation filters[C]. In: 2010 IEEE computer society conference on computer vision and pattern recognition. IEEE, pp 2544\u20132550","DOI":"10.1109\/CVPR.2010.5539960"},{"issue":"3","key":"10507_CR15","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","volume":"37","author":"JF Henriques","year":"2015","unstructured":"Henriques JF, Caseiro R, Martins P et al (2015) High-speed tracking with kernelized correlation filters[J]. IEEE Transac Pattern Analy Mach Intell 37(3):583\u2013596","journal-title":"IEEE Transac Pattern Analy Mach Intell"},{"key":"10507_CR16","doi-asserted-by":"crossref","unstructured":"Bertinetto L, Valmadre J, Golodetz S, et al (2016) Staple: Complementary learners for real-time tracking[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1401\u20131409","DOI":"10.1109\/CVPR.2016.156"},{"key":"10507_CR17","unstructured":"Wang N, Yeung DY (2013) Learning a deep compact image representation for visual tracking[C]. Advances in neural information processing systems 809\u2013817"},{"key":"10507_CR18","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"issue":"99","key":"10507_CR19","first-page":"2999","volume":"PP","author":"TY Lin","year":"2017","unstructured":"Lin TY, Goyal P, Girshick R et al (2017) Focal loss for dense object detection[J]. IEEE Transac Pattern Analy Mach Intell PP(99):2999\u20133007","journal-title":"IEEE Transac Pattern Analy Mach Intell"},{"key":"10507_CR20","unstructured":"Ren S, He K, Girshick R et al (2015) Faster r-cnn: Towards real-time object detection with region proposal networks[C]. Advances in neural information processing systems 91\u201399"},{"key":"10507_CR21","doi-asserted-by":"crossref","unstructured":"He Y, Zhu C, Wang J, et al (2019) Bounding box regression with uncertainty for accurate object detection[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2888\u20132897","DOI":"10.1109\/CVPR.2019.00300"},{"key":"10507_CR22","doi-asserted-by":"crossref","unstructured":"Rezatofighi H, Tsoi N, Gwak J Y, et al (2019) Generalized intersection over union: A metric and a loss for bounding box regression[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 658\u2013666","DOI":"10.1109\/CVPR.2019.00075"},{"key":"10507_CR23","unstructured":"Tolstikhin I, Bousquet O, Gelly S, et al (2018) Wasserstein Auto-Encoders[C]. In: International Conference on Learning Representations (ICLR 2018). OpenReview. net"},{"issue":"3","key":"10507_CR24","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[J]. Int J comput vision 115(3):211\u2013252","journal-title":"Int J comput vision"},{"key":"10507_CR25","doi-asserted-by":"crossref","unstructured":"Li B, Liu Y, Wang X (2019) Gradient harmonized single-stage detector[C]. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 33, pp 8577\u20138584","DOI":"10.1609\/aaai.v33i01.33018577"},{"key":"10507_CR26","doi-asserted-by":"crossref","unstructured":"Fan H, Lin L, Yang F, et al (2019) Lasot: A high-quality benchmark for large-scale single object tracking[C]. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5374\u20135383","DOI":"10.1109\/CVPR.2019.00552"},{"key":"10507_CR27","doi-asserted-by":"crossref","unstructured":"Danelljan M, Bhat G, Shahbaz Khan F, et al (2017) Eco: Efficient convolution operators for tracking[C]. Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6638\u20136646","DOI":"10.1109\/CVPR.2017.733"},{"key":"10507_CR28","doi-asserted-by":"crossref","unstructured":"Bhat G, Danelljan M, Gool L V, et al (2019) Learning discriminative model prediction for tracking[C]. In: Proceedings of the IEEE International Conference on Computer Vision, pp 6182\u20136191","DOI":"10.1109\/ICCV.2019.00628"},{"key":"10507_CR29","doi-asserted-by":"crossref","unstructured":"Li F, Tian C, Zuo W, et al (2018) Learning spatial-temporal regularized correlation filters for visual tracking[C]. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4904\u20134913","DOI":"10.1109\/CVPR.2018.00515"},{"key":"10507_CR30","doi-asserted-by":"crossref","unstructured":"Zhang Y, Wang L, Qi J, et al (2018) Structured siamese network for real-time visual tracking[C]. In: Proceedings of the European conference on computer vision (ECCV), pp 351\u2013366","DOI":"10.1007\/978-3-030-01240-3_22"},{"key":"10507_CR31","doi-asserted-by":"crossref","unstructured":"Choi J, Jin Chang H, Fischer T, et al (2018) Context-aware deep feature compression for high-speed visual tracking[C]. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 479\u2013488","DOI":"10.1109\/CVPR.2018.00057"},{"key":"10507_CR32","unstructured":"Zhang M, Lucas J, Ba J, et al (2019) Lookahead Optimizer: k steps forward, 1 step back[C]. Advances in Neural Information Processing Systems 9593\u20139604"},{"key":"10507_CR33","unstructured":"Kristan M, Leonardis A, Matas J, et al (2018) The sixth visual object tracking vot2018 challenge results[C]. In: Proceedings of the European Conference on Computer Vision (ECCV)"},{"key":"10507_CR34","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, et al (2017) Aggregated residual transformations for deep neural networks[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1492\u20131500","DOI":"10.1109\/CVPR.2017.634"},{"key":"10507_CR35","doi-asserted-by":"crossref","unstructured":"Ma N, Zhang X, Zheng H T, et al (2018) Shufflenet v2: Practical guidelines for efficient cnn architecture design[C]. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 116\u2013131","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"10507_CR36","first-page":"740","volume-title":"European conference on computer vision","author":"TY Lin","year":"2014","unstructured":"Lin TY, Maire M, Belongie S et al (2014) Microsoft coco: Common objects in context[C]. European conference on computer vision. Springer, Cham, pp 740\u2013755"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10507-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-021-10507-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10507-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,25]],"date-time":"2021-08-25T15:24:41Z","timestamp":1629905081000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-021-10507-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,4]]},"references-count":36,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["10507"],"URL":"https:\/\/doi.org\/10.1007\/s11063-021-10507-9","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2021,4,4]]},"assertion":[{"value":"24 March 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}