{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T05:57:21Z","timestamp":1759384641993},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,3,5]],"date-time":"2022-03-05T00:00:00Z","timestamp":1646438400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,5]],"date-time":"2022-03-05T00:00:00Z","timestamp":1646438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,5]]},"DOI":"10.1007\/s11042-022-11967-4","type":"journal-article","created":{"date-parts":[[2022,3,5]],"date-time":"2022-03-05T16:02:54Z","timestamp":1646496174000},"page":"17283-17302","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep learning of spatio-temporal information for visual tracking"],"prefix":"10.1007","volume":"81","author":[{"given":"Gwangmin","family":"Choe","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ilmyong","family":"Son","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunhwa","family":"Choe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyoson","family":"So","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyokchol","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gyongnam","family":"Choe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,5]]},"reference":[{"issue":"8","key":"11967_CR1","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1109\/TPAMI.2010.226","volume":"33","author":"B Babenko","year":"2011","unstructured":"Babenko B, Yang MH, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619\u20131632","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11967_CR2","doi-asserted-by":"crossref","unstructured":"Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr PH (2016) Staple: complementary learners for real-time tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1401\u20131409","DOI":"10.1109\/CVPR.2016.156"},{"key":"11967_CR3","doi-asserted-by":"crossref","unstructured":"Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PH (2016) Fully-convolutional siamese networks for object tracking. In: European conference on computer vision. Springer, pp 850\u2013865","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"11967_CR4","doi-asserted-by":"crossref","unstructured":"Chatfield K, Simonyan K, Vedaldi A, Zisserman A (2014) Return of the devil in the details: delving deep into convolutional nets. In: BMVC","DOI":"10.5244\/C.28.6"},{"key":"11967_CR5","doi-asserted-by":"crossref","unstructured":"Danelljan M, Hager G, Khan F, Felsberg M (2014) Accurate scale estimation for robust visual tracking. In: British machine vision conference, Nottingham, September 1-5, 2014. BMVA Press","DOI":"10.5244\/C.28.65"},{"key":"11967_CR6","doi-asserted-by":"crossref","unstructured":"Danelljan M, Hager G, Shahbaz Khan F, Felsberg M (2015) Convolutional features for correlation filter based visual tracking. In: Proceedings of the IEEE international conference on computer vision workshops, pp 58\u201366","DOI":"10.1109\/ICCVW.2015.84"},{"key":"11967_CR7","doi-asserted-by":"crossref","unstructured":"Danelljan M, Robinson A, Khan FS, Felsberg M (2016) Beyond correlation filters: Learning continuous convolution operators for visual tracking. In: European conference on computer vision. Springer, pp 472\u2013488","DOI":"10.1007\/978-3-319-46454-1_29"},{"key":"11967_CR8","doi-asserted-by":"crossref","unstructured":"Erhan D, Szegedy C, Toshev A, Anguelov D (2014) Scalable object detection using deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2147\u20132154","DOI":"10.1109\/CVPR.2014.276"},{"issue":"10","key":"11967_CR9","doi-asserted-by":"publisher","first-page":"1610","DOI":"10.1109\/TNN.2010.2066286","volume":"21","author":"J Fan","year":"2010","unstructured":"Fan J, Xu W, Wu Y, Gong Y (2010) Human tracking using convolutional neural networks. IEEE Trans Neural Netw 21(10):1610\u20131623","journal-title":"IEEE Trans Neural Netw"},{"key":"11967_CR10","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR","DOI":"10.1109\/CVPR.2014.81"},{"key":"11967_CR11","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"2","key":"11967_CR12","first-page":"354","volume":"47","author":"Z He","year":"2017","unstructured":"He Z, Yi S, Cheung YM, You X, Tang YY (2017) Robust object tracking via key patch sparse representation. IEEE Trans Cybern 47(2):354\u2013364","journal-title":"IEEE Trans Cybern"},{"key":"11967_CR13","unstructured":"Hong S, You T, Kwak S, Han B (2015) Online tracking by learning discriminative saliency map with convolutional neural network. In: International conference on machine learning, pp 597\u2013606"},{"key":"11967_CR14","unstructured":"Hong S, You T, Kwak S, Han B (2015) Online tracking by learning discriminative saliency map with convolutional neural network. In: ICML"},{"key":"11967_CR15","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"key":"11967_CR16","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: NIPS"},{"key":"11967_CR17","doi-asserted-by":"crossref","unstructured":"Li H, Li Y, Porikli F (2014) DeepTrackL Learning discriminative feature representations by convolutional neural networks for visual tracking. In: BMVC","DOI":"10.5244\/C.28.56"},{"key":"11967_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2015.03.067","volume":"315","author":"L Liu","year":"2015","unstructured":"Liu L, Chen CP, Zhou Y, You X (2015) A new weighted mean filter with a two-phase detector for removing impulse noise. Inform Sci 315:1\u201316","journal-title":"Inform Sci"},{"key":"11967_CR19","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.knosys.2017.07.032","volume":"134","author":"Q Liu","year":"2017","unstructured":"Liu Q, Lu X, He Z, Zhang C, Chen WS (2017) Deep convolutional neural networks for thermal infrared object tracking. Knowl-Based Syst 134:189\u2013198","journal-title":"Knowl-Based Syst"},{"issue":"5","key":"11967_CR20","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1007\/s11760-016-1035-x","volume":"11","author":"Q Liu","year":"2017","unstructured":"Liu Q, Ma X, Ou W, Zhou Q (2017) Visual object tracking with online sample selection via lasso regularization. Sig Image Video Process 11(5):881\u2013888","journal-title":"Sig Image Video Process"},{"key":"11967_CR21","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: CVPR","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"11967_CR22","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"11967_CR23","doi-asserted-by":"crossref","unstructured":"Ma C, Huang JB, Yang X, Yang MH (2015) Hierarchical convolutional features for visual tracking. In: Proceedings of the IEEE international conference on computer vision, pp 3074\u20133082","DOI":"10.1109\/ICCV.2015.352"},{"issue":"5","key":"11967_CR24","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/s00138-018-0930-2","volume":"29","author":"X Ma","year":"2018","unstructured":"Ma X, Liu Q, Ou W, Zhou Q (2018) Visual object tracking via coefficients constrained exclusive group lasso. Mach Vis Appl 29(5):749\u2013763","journal-title":"Mach Vis Appl"},{"key":"11967_CR25","doi-asserted-by":"crossref","unstructured":"Moujtahid S, Duffner S, Baskurt A (2015) Classifying global scene context for on-line multiple tracker selection. In: British machine vision conference (BMVC)","DOI":"10.5244\/C.29.163"},{"key":"11967_CR26","doi-asserted-by":"crossref","unstructured":"Ondruska P, Posner I (2016) Deep tracking: Seeing beyond seeing using recurrent neural networks. arXiv:1602.00991","DOI":"10.1609\/aaai.v30i1.10413"},{"key":"11967_CR27","doi-asserted-by":"crossref","unstructured":"Possegger H, Mauthner T, Bischof H (2015) In defense of color-based model-free tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2113\u20132120","DOI":"10.1109\/CVPR.2015.7298823"},{"key":"11967_CR28","doi-asserted-by":"crossref","unstructured":"Qi Y, Zhang S, Qin L, Yao H, Huang Q, Lim J, Yang MH (2016) Hedged deep tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4303\u20134311","DOI":"10.1109\/CVPR.2016.466"},{"key":"11967_CR29","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.neunet.2017.11.021","volume":"105","author":"B Sun","year":"2018","unstructured":"Sun B, Cao S, He J, Yu L (2018) Affect recognition from facial movements and body gestures by hierarchical deep spatio-temporal features and fusion strategy. Neural Netw 105:36\u201351","journal-title":"Neural Netw"},{"issue":"7","key":"11967_CR30","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, Calderara S, Dehghan A, Shah M (2014) Visual tracking: an experimental survey. IEEE Trans Pattern Anal Mach Intell 36(7):1442\u20131468","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11967_CR31","doi-asserted-by":"crossref","unstructured":"Taigman Y, Yang M, Ranzato M, Wolf L (2014) Deepface: closing the gap to human-level performance in face verification. In: CVPR","DOI":"10.1109\/CVPR.2014.220"},{"key":"11967_CR32","doi-asserted-by":"crossref","unstructured":"Toshev A, Szegedy C (2014) Deeppose: human pose estimation via deep neural networks. In: CVPR","DOI":"10.1109\/CVPR.2014.214"},{"key":"11967_CR33","doi-asserted-by":"crossref","unstructured":"Valmadre J, Bertinetto L, Henriques J, Vedaldi A, Torr PH (2017) End-to-end representation learning for correlation filter based tracking. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 5000\u20135008","DOI":"10.1109\/CVPR.2017.531"},{"key":"11967_CR34","unstructured":"Wang N, Yeung DY (2013) Learning a deep compact image representation for visual tracking. In: Advances in neural information processing systems, pp 809\u2013817"},{"key":"11967_CR35","doi-asserted-by":"crossref","unstructured":"Wu Y, Lim J, Yang MH (2013) Online object tracking: A benchmark. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2411\u20132418","DOI":"10.1109\/CVPR.2013.312"},{"issue":"9","key":"11967_CR36","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","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"7","key":"11967_CR37","doi-asserted-by":"publisher","first-page":"2584","DOI":"10.1016\/j.patcog.2012.01.016","volume":"45","author":"A Yao","year":"2012","unstructured":"Yao A, Lin X, Wang G, Yu S (2012) A compact association of particle filtering and kernel based object tracking. Pattern Recogn 45(7):2584\u20132597","journal-title":"Pattern Recogn"},{"issue":"4","key":"11967_CR38","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 (CSUR) 38(4):13","journal-title":"ACM Comput Surv (CSUR)"},{"key":"11967_CR39","doi-asserted-by":"crossref","unstructured":"Zuo W, Wu X, Lin L, Zhang L, Yang MH (2018) Learning support correlation filters for visual tracking. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2018.2829180"},{"key":"11967_CR40","doi-asserted-by":"crossref","unstructured":"Zhang K, Zhang L, Liu Q, Zhang D, Yang MH (2014) Fast visual tracking via dense spatio-temporal context learning. In: European conference on computer vision. Springer, pp 127\u2013141","DOI":"10.1007\/978-3-319-10602-1_9"},{"key":"11967_CR41","doi-asserted-by":"crossref","unstructured":"Zhang J, Ma S, Sclaroff S (2014) Meem: robust tracking via multiple experts using entropy minimization. In: European conference on computer vision. Springer, pp 188\u2013203","DOI":"10.1007\/978-3-319-10599-4_13"},{"key":"11967_CR42","doi-asserted-by":"crossref","unstructured":"Zhang L, Varadarajan J, Suganthan PN, Ahuja N, Moulin P (2017) Robust visual tracking using oblique random forests. In: IEEE international conference on computer vision and pattern recognition. IEEE","DOI":"10.1109\/CVPR.2017.617"},{"issue":"S1","key":"11967_CR43","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1080\/24699322.2018.1560097","volume":"24","author":"Z Zhao","year":"2019","unstructured":"Zhao Z, Chen Z, Voros S, Cheng X (2019) Real-time tracking of surgical instruments based on spatio-temporal context and deep learning. Comput Assist Sugery 24(S1):20\u201329","journal-title":"Comput Assist Sugery"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-11967-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-11967-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-11967-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T12:55:54Z","timestamp":1674910554000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-11967-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,5]]},"references-count":43,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["11967"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-11967-4","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,5]]},"assertion":[{"value":"12 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}