Abstract
This paper proposes the adaptive multi-kernel support correlation filters with hedge parameter strategy and temporal filtering constraint for real-time tracking. In order to fuse the excellent properties of various views that characterize the object robust appearance accurately, support correlation filtering responses from multiple kernels can be adaptively integrated into one strong and accurate filtering response map by hedge parameter strategy in a parallel way. It absorbs the strongly discriminative ability from different feature-based support correlation filters, which tolerate sampling outliers of circulant structures with the help of SVM learning way. Also, it exploits the intense information of multi-view appearance representations which guarantee the fusion of reliable correlation filtering maps with reasonable parameters. Meanwhile, with the temporal filtering constraint to maintain historical appearance characteristics, alternating fixed-point algorithm improves complementary memory-updated model that keeps the stability of tracking process continuously and alleviates the target drifting situation for each support correlation filter. Experimental results demonstrate that the proposed approach achieves favorable performance on multiple dynamic scenes.
Similar content being viewed by others
References
Avidan S (2004) Support vector tracking. IEEE Trans Pattern Anal Mach Intell 26(8):1064–1072
Avidan S (2007) Ensemble tracking. IEEE Trans Pattern Anal Mach Intell 29(2):261–271
Bai Y, Tang M (2011) Robust visual tracking via ranking SVM. In: Macq B, Schelkens P (eds) 18th IEEE international conference on image processing, ICIP 2011. IEEE, Brussels, pp 517–520
Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr P H S (2016) Staple: Complementary learners for real-time tracking. In: 2016 IEEE conference on computer vision and pattern recognition, CVPR 2016. IEEE Computer Society, Las Vegas, pp 1401–1409
Bertinetto L, Valmadre J, Henriques J F, Vedaldi A, Torr P H S (2016) Fully-convolutional siamese networks for object tracking. In: Hua G, Jégou H (eds) Computer vision - ECCV 2016 workshops - amsterdam, The netherlands, proceedings, part II, Lecture Notes in Computer Science, vol 9914, pp 850–865
Bibi A, Ghanem B (2015) Multi-template scale-adaptive kernelized correlation filters. In: 2015 IEEE international conference on computer vision workshop, ICCV Workshops 2015. IEEE Computer Society, Santiago, pp 613–620
Bibi A, Mueller M, Ghanem B (2016) Target response adaptation for correlation filter tracking. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer vision - ECCV 2016 - 14th european conference, Amsterdam, proceedings, part VI, Lecture Notes in Computer Science, vol 9910. Springer, pp 419–433
Boddeti V N, Kumar B V K V (2014) Maximum margin vector correlation filter. CoRR arXiv:1404.6031
Bolme D S, Beveridge J R, Draper B A, Lui Y M (2010) Visual object tracking using adaptive correlation filters. In: The twenty-third ieee conference on computer vision and pattern recognition, CVPR 2010. IEEE Computer Society, San Francisco, pp 2544–2550
Cai B, Xu X, Xing X, Jia K, Miao J, Tao D (2016) BIT: biologically inspired tracker. IEEE Trans Image Process 25(3):1327–1339
Chaudhuri K, Freund Y, Hsu D J (2009) A parameter-free hedging algorithm. In: Bengio Y, Schuurmans D, Lafferty J D, Williams C K I, Culotta A (eds) Advances in neural information processing systems 22: 23rd annual conference on neural information processing systems 2009. Proceedings of a meeting held. Curran Associates, Inc., Vancouver, pp 297–305
Dai K, Wang D, Lu H, Sun C, Li J (2019) Visual tracking via adaptive spatially-regularized correlation filters. In: IEEE conference on computer vision and pattern recognition, CVPR 2019. Computer Vision Foundation / IEEE, Long Beach, pp 4670–4679
Danelljan M, Häger G, Khan F S, Felsberg M (2014) Accurate scale estimation for robust visual tracking. In: Valstar M F, French A P, Pridmore T P (eds) British machine vision conference, BMVC 2014. BMVA Press, Nottingham
Danelljan M, Häger G, Khan F S, Felsberg M (2015) Convolutional features for correlation filter based visual tracking. In: 2015 IEEE international conference on computer vision workshop, ICCV Workshops 2015. IEEE Computer Society, Santiago, pp 621–629
Danelljan M, Häger G, Khan F S, Felsberg M (2016) Learning spatially regularized correlation filters for visual tracking. CoRR arXiv:1608.05571
Danelljan M, Robinson A, Khan F S, Felsberg M (2016) Beyond correlation filters: Learning continuous convolution operators for visual tracking. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer vision - ECCV 2016 - 14th European conference, proceedings, part V. Lecture Notes in Computer Science, vol 9909. Springer, Amsterdam, pp 472–488
Danelljan M, Häger G, Khan F S, Felsberg M (2017) Discriminative scale space tracking. IEEE Trans Pattern Anal Mach Intell 39(8):1561–1575
Danelljan M, Bhat G, Khan F S, Felsberg M (2017) ECO: efficient convolution operators for tracking. In: 2017 IEEE conference on computer vision and pattern recognition, CVPR 2017. IEEE Computer Society, Honolulu, pp 6931–6939
Ding G, Chen W, Zhao S, Han J, Liu Q (2018) Real-time scalable visual tracking via quadrangle kernelized correlation filters. IEEE Trans Intell Transp Syst 19(1):140–150
Dollar P (2015) Piotr’s computer vision matlab toolbox. línea]. Available: https://github.com/pdollar/toolbox
Fan H, Ling H (2017) Sanet: Structure-aware network for visual tracking. In: 2017 IEEE conference on computer vision and pattern recognition workshops, CVPR Workshops 2017. IEEE Computer Society, Honolulu, pp 2217–2224
Galoogahi H K, Fagg A, Lucey S (2017) Learning background-aware correlation filters for visual tracking. In: IEEE international conference on computer vision, ICCV 2017. IEEE Computer Society, Venice, pp 1144–1152
Gan Q, Guo Q, Zhang Z, Cho K (2015) First step toward model-free, anonymous object tracking with recurrent neural networks. CoRR arXiv:1511.06425
Hare S, Golodetz S, Saffari A, Vineet V, Cheng M-M, Hicks S L, Torr P H S (2016) Struck: Structured output tracking with kernels. IEEE Trans Pattern Anal Mach Intell 38(10):2096–2109
Held D, Thrun S, Savarese S (2016) Learning to track at 100 FPS with deep regression networks. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer vision - ECCV 2016 - 14th European conference, proceedings, part I, Lecture Notes in Computer Science, vol 9905. Springer, Amsterdam, pp 749–765
Henriques J F, Caseiro R, Martins P, Batista J P (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: Fitzgibbon A W, Lazebnik S, Perona P, Sato Y, Schmid C (eds) Computer vision - ECCV 2012 - 12th European conference on computer vision, proceedings, part IV, Lecture Notes in Computer Science, vol 7575. Springer, Florence, pp 702–715
Henriques J F, Carreira J , Caseiro R, Batista J (2013) Beyond hard negative mining: Efficient detector learning via block-circulant decomposition. In: IEEE international conference on computer vision, ICCV 2013. IEEE Computer Society, Sydney, pp 2760–2767
Henriques J F, Caseiro R, Martins P, Batista J (2015) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583–596
Hu H, Ma B, Shen J, Sun H, Shao L, Porikli F (2019) Robust object tracking using manifold regularized convolutional neural networks. IEEE Trans Multim 21(2):510–521
Ji Z, Feng K, Qian Y (2019) Part-based visual tracking via structural support correlation filter. J Vis Commun Image Represent 64
Jung I, Son J, Baek M, Han B (2018) Real-time mdnet. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer vision - ECCV 2018 - 15th European conference, proceedings, part IV, Lecture Notes in Computer Science, vol 11208. Springer, Munich, pp 89–104
Kahou S E, Michalski V, Memisevic R, Pal C J, Vincent P (2017) RATM: recurrent attentive tracking model. In: 2017 IEEE conference on computer vision and pattern recognition workshops, CVPR Workshops 2017. IEEE Computer Society, Honolulu, pp 1613–1622
Kalal Z (2011) Tracking learning detection. Ph.D. Thesis, University of Surrey, Guildford
Li Y, Zhu J (2014) A scale adaptive kernel correlation filter tracker with feature integration. In: Agapito L, Bronstein M M, Rother C (eds) Computer vision - ECCV 2014 workshops, proceedings, part II, Lecture Notes in Computer Science, vol 8926. Springer, Zurich, pp 254–265
Li Y, Zhu J, Hoi S C H (2015) Reliable patch trackers: Robust visual tracking by exploiting reliable patches. In: IEEE conference on computer vision and pattern recognition, CVPR 2015. IEEE Computer Society, Boston, pp 353–361
Li B, Yan J, Wu W, Zhu Z, Hu X (2018) High performance visual tracking with siamese region proposal network. In: 2018 IEEE conference on computer vision and pattern recognition, CVPR 2018. IEEE Computer Society, Salt Lake City, pp 8971–8980
Li B, Wu W, Wang Q, Zhang F, Xing J, Yan J (2019) Siamrpn++: Evolution of siamese visual tracking with very deep networks. In: IEEE conference on computer vision and pattern recognition, CVPR 2019. Computer Vision Foundation / IEEE, Long Beach, pp 4282–4291
Lin Y, Zhong B, Li G, Zhao S, Chen Z, Fan W (2019) Localization-aware meta tracker guided with adversarial features. IEEE Access 7:99441–99450
Liu G, Lin Z, Yan S, Sun J, Yu Y, Ma Y (2013) Robust recovery of subspace structures by low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(1):171–184
Liu T, Wang G, Yang Q (2015) Real-time part-based visual tracking via adaptive correlation filters. In: IEEE conference on computer vision and pattern recognition, CVPR 2015. IEEE Computer Society, Boston, pp 4902–4912
Lu X, Ni B, Ma C, Yang X (2019) Learning transform-aware attentive network for object tracking. Neurocomputing 349:133–144
Ma C, Huang J-B, Yang X, Yang M-H (2015) Hierarchical convolutional features for visual tracking. In: 2015 IEEE international conference on computer vision, ICCV 2015. IEEE Computer Society, Santiago, pp 3074–3082
Ma C, Yang X, Zhang C, Yang M-H (2015) Long-term correlation tracking. In: IEEE conference on computer vision and pattern recognition, CVPR 2015. IEEE Computer Society, Boston, pp 5388–5396
Mei X, Hong Z, Prokhorov D V, Tao D (2015) Robust multitask multiview tracking in videos. IEEE Trans Neural Netw Learn Syst 26(11):2874–2890
Mueller M, Smith N, Ghanem B (2017) Context-aware correlation filter tracking. In: 2017 IEEE conference on computer vision and pattern recognition, CVPR 2017. IEEE Computer Society, Honolulu, pp 1387–1395
Nam H, Baek M, Han B (2016) Modeling and propagating cnns in a tree structure for visual tracking. CoRR arXiv:1608.07242
Nam H, Han B (2016) Learning multi-domain convolutional neural networks for visual tracking. In: 2016 IEEE conference on computer vision and pattern recognition, CVPR 2016. IEEE Computer Society, Las Vegas, pp 4293–4302
Ning J, Yang J, Jiang S, Zhang L, Yang M-H (2016) Object tracking via dual linear structured SVM and explicit feature map. In: 2016 IEEE conference on computer vision and pattern recognition, CVPR 2016. IEEE Computer Society, Las Vegas, pp 4266–4274
Qi Y, Zhang S, Qin L, Huang Q, Yao H, Lim J, Yang M-H (2019) Hedging deep features for visual tracking. IEEE Trans Pattern Anal Mach Intell 41(5):1116–1130
Radford A, Metz L, Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. In: Bengio Y, LeCun Y (eds) 4th international conference on learning representations, ICLR 2016. Conference Track Proceedings, San Juan
Rao C, Yao C, Bai X, Qiu W, Liu W (2012) Online random ferns for robust visual tracking. In: Proceedings of the 21st international conference on pattern recognition, ICPR 2012. IEEE Computer Society, Tsukuba, pp 1447–1450
Roffo G, Melzi S (2016) Online feature selection for visual tracking. In: Wilson R C, Hancock E R, Smith W A P (eds) Proceedings of the British machine vision conference 2016, BMVC 2016. BMVA Press, New York
Ross D A, Lim J, Lin R-S, Yang M-H (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1-3):125–141
Su Z, Li J, Chang J, Du B, Xiao Y (2020) Real-time visual tracking using complementary kernel support correlation filters. Frontiers Comput Sci 14(2):417–429
Sui Y, Wang G, Zhang L (2018) Correlation filter learning toward peak strength for visual tracking. IEEE Trans Cybern 48(4):1290–1303
Tao R, Gavves E, Smeulders A W M (2016) Siamese instance search for tracking. In: 2016 IEEE conference on computer vision and pattern recognition, CVPR 2016. IEEE Computer Society, Las Vegas, pp 1420–1429
Valmadre J, Bertinetto L, Henriques J F, Vedaldi A, Torr P H S (2017) End-to-end representation learning for correlation filter based tracking. In: 2017 IEEE conference on computer vision and pattern recognition, CVPR 2017. IEEE Computer Society, Honolulu, pp 5000–5008
Vedaldi A, Lenc K (2015) Matconvnet: Convolutional neural networks for matlab. In: Proceedings of the 23rd ACM international conference on multimedia, pp 689–692
Wang D, Lu H (2012) Object tracking via 2dpca and l1 regularization. IEEE Signal Process Lett 19(11):711–714
Wang D, Lu H (2014) Visual tracking via probability continuous outlier model. In: 2014 IEEE conference on computer vision and pattern recognition, CVPR 2014. IEEE Computer Society, Columbus, pp 3478–3485
Wang G, Qin X, Zhong F, Liu Y, Li H, Peng Q, Yang M-H (2015) Visual tracking via sparse and local linear coding. IEEE Trans Image Process 24(11):3796–3809
Wang L, Ouyang W, Wang X, Lu H (2015) Visual tracking with fully convolutional networks. In: 2015 IEEE international conference on computer vision, ICCV 2015. IEEE Computer Society, Santiago, pp 3119–3127
Wang N, Wang J, Yeung D-Y (2013) Online robust non-negative dictionary learning for visual tracking. In: IEEE international conference on computer vision, ICCV 2013. IEEE Computer Society, Sydney, pp 657–664
Wang X, Shrivastava A, Gupta A (2017) A-fast-rcnn: Hard positive generation via adversary for object detection. In: 2017 IEEE conference on computer vision and pattern recognition, CVPR 2017. IEEE Computer Society, Honolulu, pp 3039–3048
Wang C, Zhang L, Xie L, Yuan J (2018) Kernel cross-correlator. In: McIlraith S A, Weinberger K Q (eds) Proceedings of the thirty-second aaai conference on artificial intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18). AAAI Press, New Orleans, pp 4179–4186
Wu Y, Lim J, Yang M-H (2013) Online object tracking: A benchmark. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Xu T, Feng Z-H, Wu X-J, Kittler J (2019) Learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking. IEEE Trans Image Process 28(11):5596–5609
Yilmaz A, Javed O, Shah M (2006) Object tracking:a survey. ACM Comput Surv 38(4):13
Yin M, Gao J, Lin Z (2016) Laplacian regularized low-rank representation and its applications. IEEE Trans Pattern Anal Mach Intell 38(3):504–517
Zhang K, Zhang L, Yang M-H (2012) Real-time compressive tracking. In: Fitzgibbon A W, Lazebnik S, Perona P, Sato Y, Schmid C (eds) Computer vision - ECCV 2012 - 12th European conference on computer vision, proceedings, part III, Lecture Notes in Computer Science, vol 7574. Springer, Florence, pp 864–877
Zhang K, Zhang L, Yang M-H, Zhang D (2013) Fast tracking via spatio-temporal context learning. CoRR arXiv:1311.1939
Zhang K, Song H (2013) Real-time visual tracking via online weighted multiple instance learning. Pattern Recognit 46(1):397–411
Zhang J, Liu K, Cheng F, Li Y (2014) Visual tracking with randomly projected ferns. Signal Process Image Commun 29(9):987–997
Zhang S, Sui Y, Yu X, Zhao S, Zhang L (2015) Hybrid support vector machines for robust object tracking. Pattern Recognit 48(8):2474–2488
Zhang S, Yu X, Sui Y, Zhao S, Zhang L (2015) Object tracking with multi-view support vector machines. IEEE Trans Multimed 17(3):265–278
Zhang S, Zhao S, Sui Y, Zhang L (2015) Single object tracking with fuzzy least squares support vector machine. IEEE Trans Image Process 24 (12):5723–5738
Zhang B, Li Z, Cao X, Ye Q, Chen C, Shen L, Perina A, Ji R (2017) Output constraint transfer for kernelized correlation filter in tracking. IEEE Trans Syst Man Cybern Syst 47(4):693–703
Zhang L, Suganthan P N (2017) Robust visual tracking via co-trained kernelized correlation filters. Pattern Recognit 69:82–93
Zhang L, Varadarajan J, Suganthan P N, Ahuja N, Moulin P (2017) Robust visual tracking using oblique random forests. In: 2017 IEEE conference on computer vision and pattern recognition, CVPR 2017. IEEE Computer Society, Honolulu, pp 5825–5834
Zhang S, Sui Y, Zhao S, Zhang L (2017) Graph-regularized structured support vector machine for object tracking. IEEE Trans Circ Syst Video Techn 27(6):1249–1262
Zhong W, Lu H, Yang M-H (2014) Robust object tracking via sparse collaborative appearance model. IEEE Trans Image Process 23(5):2356–2368
Zhu S, Fang Z, Gao F (2018) Hierarchical convolutional features for end-to-end representation-based visual tracking. Mach Vis Appl 29(6):955–963
Zuo W, Wu X, Lin L, Zhang L, Yang M-H (2019) Learning support correlation filters for visual tracking. IEEE Trans Pattern Anal Mach Intell 41(5):1158–1172
Acknowledgments
This work was supported by ‘Leading Talents of Shandong University of Science and Technology’, ‘863 project Physical Model Based Dynamic Evolution Technology of Complex Scene’ (2015AA016404), ‘Shandong Province Higher Educational Science and Technology Program’ (J17KA075) and ‘National Nature Science Foundation of China’ (61801270).
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
An, X., Liang, Q. & Sun, N. Multi-kernel support correlation filters with temporal filtering constraint for object tracking. Multimed Tools Appl 80, 14041–14073 (2021). https://doi.org/10.1007/s11042-020-10345-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-10345-2