{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:21:11Z","timestamp":1740108071547,"version":"3.37.3"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2017,3,8]],"date-time":"2017-03-08T00:00:00Z","timestamp":1488931200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61432012","U1435213"],"award-info":[{"award-number":["61432012","U1435213"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1007\/s00521-017-2906-y","type":"journal-article","created":{"date-parts":[[2017,3,8]],"date-time":"2017-03-08T05:50:07Z","timestamp":1488952207000},"page":"3317-3326","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Tracking topology structure adaptively with deep neural networks"],"prefix":"10.1007","volume":"30","author":[{"given":"Xueying","family":"Shi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangyong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pheng Ann","family":"Heng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhang","family":"Yi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,3,8]]},"reference":[{"key":"2906_CR1","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE Computer Society conference on computer vision and pattern recognition (CVPR), San Diego, USA, pp 886\u2013893","DOI":"10.1109\/CVPR.2005.177"},{"key":"2906_CR2","doi-asserted-by":"crossref","unstructured":"Tang F, Brennan S, Zhao Q, Tao H (2007) Co-tracking using semi-supervised support vector machines. In: IEEE international conference on computer vision (ICCV), Rio de Janeiro, Brazil, pp 1\u20138","DOI":"10.1109\/ICCV.2007.4408954"},{"key":"2906_CR3","doi-asserted-by":"crossref","first-page":"2259","DOI":"10.1109\/TPAMI.2011.66","volume":"33","author":"X Mei","year":"2011","unstructured":"Mei X, Ling H (2011) Robust visual tracking and vehicle classification via sparse representation. IEEE Trans Pattern Anal Mach Intell (PAMI) 33:2259\u20132272","journal-title":"IEEE Trans Pattern Anal Mach Intell (PAMI)"},{"key":"2906_CR4","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s11263-007-0075-7","volume":"77","author":"DA Ross","year":"2008","unstructured":"Ross DA, Lim J, Lin R-S, Yang M-H (2008) Incremental learning for robust visual tracking. Int J Comput Vis (IJCV) 77:125\u2013141","journal-title":"Int J Comput Vis (IJCV)"},{"key":"2906_CR5","unstructured":"Zhang T, Ghanem B, Liu S, Ahuja N (2012) Robust visual tracking via multi-task sparse learning. In: IEEE conference on computer vision and pattern recognition (CVPR), Rhode Island, USA, pp 2042\u20132049"},{"key":"2906_CR6","doi-asserted-by":"crossref","first-page":"2356","DOI":"10.1109\/TIP.2014.2313227","volume":"23","author":"W Zhong","year":"2014","unstructured":"Zhong W, Lu H, Yang M-H (2014) Robust object tracking via sparse collaborative appearance model. IEEE Trans Image Process (TIP) 23:2356\u20132368","journal-title":"IEEE Trans Image Process (TIP)"},{"key":"2906_CR7","unstructured":"Mei X, Ling H, Wu Y, Blasch E, Bai L (2011) Minimum error bounded efficient \n                        $$\\ell $$\n                        \n                            \n                                \u2113\n                            \n                        \n                     1 tracker with occlusion detection. In: IEEE computer society conference on computer vision and pattern recognition (CVPR). Colorado Springs, USA, pp 1257\u20131264"},{"key":"2906_CR8","unstructured":"Wang N, Li S, Gupta A, Yeung D-Y (2015) Transferring rich feature hierarchies for robust visual tracking. arXiv preprint \n                        arXiv:1501.04587"},{"key":"2906_CR9","doi-asserted-by":"crossref","unstructured":"Wang L, Ouyang W, Wang X, Lu H (2015) Visual tracking with fully convolutional networks. In: IEEE international conference on computer vision (ICCV), Santiago, Chile, pp 3119\u20133127","DOI":"10.1109\/ICCV.2015.357"},{"key":"2906_CR10","doi-asserted-by":"crossref","unstructured":"Ma C, Huang J-B, Yang X, Yang M-H (2015) Hierarchical convolutional features for visual tracking. In: IEEE international conference on computer vision (ICCV), Santiago, Chile, pp 3074\u20133082","DOI":"10.1109\/ICCV.2015.352"},{"key":"2906_CR11","doi-asserted-by":"crossref","unstructured":"Adam A, Rivlin E, Shimshoni I (2006) Robust fragments-based tracking using the integral histogram. In: IEEE computer society conference on computer vision and pattern recognition (CVPR), New York, USA, pp 798\u2013805","DOI":"10.1109\/CVPR.2006.256"},{"key":"2906_CR12","doi-asserted-by":"crossref","unstructured":"Kwon J, Lee KM (2009) Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive basin hopping Monte Carlo sampling. In: IEEE conference on computer vision and pattern recognition (CVPR), Miami, USA, pp 1208\u20131215","DOI":"10.1109\/CVPR.2009.5206502"},{"key":"2906_CR13","doi-asserted-by":"crossref","unstructured":"Yang S, Luo P, Loy C-C, Tang X (2015) From facial parts responses to face detection: a deep learning approach. In: IEEE international conference on computer vision (ICCV), Santiago, Chile, pp 3676\u20133684","DOI":"10.1109\/ICCV.2015.419"},{"key":"2906_CR14","doi-asserted-by":"crossref","unstructured":"Wang N, Shi J, Yeung D-Y, Jia J (2015) Understanding and diagnosing visual tracking systems. In: IEEE international conference on computer vision (ICCV), Santiago, Chile, pp 3101\u20133109","DOI":"10.1109\/ICCV.2015.355"},{"key":"2906_CR15","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","volume":"37","author":"Y Wu","year":"2015","unstructured":"Wu Y, Lim J, Yang M-H (2015) Object tracking benchmark. IEEE Trans Pattern Anal Mach Intell (PAMI) 37:1834\u20131848","journal-title":"IEEE Trans Pattern Anal Mach Intell (PAMI)"},{"key":"2906_CR16","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: IEEE conference on computer vision and pattern recognition (CVPR), Columbus, USA, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"2906_CR17","unstructured":"Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y (2014) Overfeat: integrated recognition, localization and detection using convolutional networks. In: International conference on learning representations (ICLR), Banff, Canada"},{"key":"2906_CR18","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: IEEE conference on computer vision and pattern recognition (CVPR), Boston, USA, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2906_CR19","unstructured":"Ouyang W, Luo P, Zeng X, Qiu S, Tian Y, Li H, Yang S, Wang Z, Xiong Y, Qian C, Zhu Z, Wang R, Loy C-C, Wang X, Tang X (2014) Deepid-net: multi-stage and deformable deep convolutional neural networks for object detection. arXiv preprint \n                        arXiv:1409.3505"},{"key":"2906_CR20","unstructured":"Lin M, Chen Q, Yan S (2014) Network in network. In: International conference on learning representations (ICLR), Banff, Canada"},{"key":"2906_CR21","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations (ICLR), Banff, Canada"},{"key":"2906_CR22","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2014) Spatial pyramid pooling in deep convolutional networks for visual recognition. In: European conference on computer vision (ECCV), Zurich, Switzerland, pp 346\u2013361","DOI":"10.1007\/978-3-319-10578-9_23"},{"key":"2906_CR23","unstructured":"Wang N, Yeung D-Y (2013) Learning a deep compact image representation for visual tracking. In: Advances in Neural Information Processing Systems 26 (NIPS 2013), Lake Tahoe, USA, pp 809\u2013817"},{"key":"2906_CR24","doi-asserted-by":"crossref","first-page":"1958","DOI":"10.1109\/TPAMI.2008.128","volume":"30","author":"A Torralba","year":"2008","unstructured":"Torralba A, Fergus R, Freeman WT (2008) 80 million tiny images: a large data set for nonparametric object and scene recognition. IEEE Trans Pattern Anal Mach Intell (PAMI) 30:1958\u20131970","journal-title":"IEEE Trans Pattern Anal Mach Intell (PAMI)"},{"key":"2906_CR25","unstructured":"Jia X, Lu H, Yang M-H (2012) Visual tracking via adaptive structural local sparse appearance model. In: IEEE conference on computer vision and pattern recognition (CVPR), Rhode Island, USA, pp 1822\u20131829"},{"key":"2906_CR26","doi-asserted-by":"crossref","unstructured":"Fischer A, Igel C (2012) An introduction to restricted Boltzmann machines. In: Alvarez L, Mejail ME, Gomez LE, Jacobo JE (eds) 17th Iberoamerican Congress, CIARP 2012, Buenos Aires, Argentina, pp 14\u201336","DOI":"10.1007\/978-3-642-33275-3_2"},{"key":"2906_CR27","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/78.978374","volume":"50","author":"MS Arulampalam","year":"2002","unstructured":"Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear\/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50:174\u2013188","journal-title":"IEEE Trans Signal Process"},{"key":"2906_CR28","doi-asserted-by":"crossref","unstructured":"Hare S, Saffari A, Torr PHS (2011) Struck: Structured output tracking with kernels. In: IEEE international conference on computer vision (ICCV), Rhode Island, USA, pp 263\u2013270","DOI":"10.1109\/ICCV.2011.6126251"},{"key":"2906_CR29","doi-asserted-by":"crossref","unstructured":"Henriques JF, Caseiro R, Martins P, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: European conference on computer vision (ECCV), Firenze, Italy, pp 702\u2013715","DOI":"10.1007\/978-3-642-33765-9_50"},{"key":"2906_CR30","doi-asserted-by":"crossref","unstructured":"Zhang K, Zhang L, Yang M-H (2012) Real-time compressive tracking. In: European conference on computer vision (ECCV), Firenze, Italy, pp 864\u2013877","DOI":"10.1007\/978-3-642-33712-3_62"},{"key":"2906_CR31","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s11263-014-0740-6","volume":"111","author":"S Oron","year":"2015","unstructured":"Oron S, Bar-Hillel A, Levi D, Avidan S (2015) Locally orderless tracking. Int J Comput Vis (IJCV) 111:213\u2013228","journal-title":"Int J Comput Vis (IJCV)"},{"key":"2906_CR32","doi-asserted-by":"crossref","unstructured":"Dinh TB, Vo N, Medioni G (2011) Context tracker: exploring supporters and distracters in unconstrained environments. In: IEEE conference on computer vision and pattern recognition (CVPR), Colorado, USA, pp 1177\u20131184","DOI":"10.1109\/CVPR.2011.5995733"},{"key":"2906_CR33","doi-asserted-by":"crossref","unstructured":"Sevilla-Lara L, Learned-Miller E (2012) Distribution fields for tracking. In: IEEE conference on computer vision and pattern recognition (CVPR), Rhode Island, USA, pp 1910\u20131917","DOI":"10.1109\/CVPR.2012.6247891"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-017-2906-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-2906-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-2906-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,11,19]],"date-time":"2018-11-19T04:41:59Z","timestamp":1542602519000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-017-2906-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,8]]},"references-count":33,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2018,12]]}},"alternative-id":["2906"],"URL":"https:\/\/doi.org\/10.1007\/s00521-017-2906-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2017,3,8]]}}}