A parallel and robust object tracking approach synthesizing adaptive Bayesian learning and improved incremental subspace learning

Abstract

This paper presents a novel tracking algorithm which integrates two complementary trackers. Firstly, an improved Bayesian tracker(B-tracker) with adaptive learning rate is presented. The classification score of B-tracker reflects tracking reliability, and a low score usually results from large appearance change. Therefore, if the score is low, we decrease the learning rate to update the classifier fast so that B-tracker can adapt to the variation and vice versa. In this way, B-tracker is more suitable than its traditional version to solve appearance change problem. Secondly, we present an improved incremental subspace learning method tracker(Stracker). We propose to calculate projected coordinates using maximum posterior probability, which results in a more accurate reconstruction error than traditional subspace learning tracker. Instead of updating at every time, we present a stopstrategy to deal with occlusion problem. Finally, we present an integrated framework(BAST), in which the pair of trackers run in parallel and return two candidate target states separately. For each candidate state, we define a tracking reliability metrics to measure whether the candidate state is reliable or not, and the reliable candidate state will be chosen as the target state at the end of each frame. Experimental results on challenging sequences show that the proposed approach is very robust and effective in comparison to the state-of-the-art trackers.

Keywords

object tracking / Bayesian learning / subspace learning / particle filter / principal component analysis

References

[1] Ali A, Jalil A, Niu J, Zhao X, Rathore S, Ahmed J, Iftikhar M A. Visual object tracking–classical and contemporary approaches. Frontiers of Computer Science, 2016, 10(1): 167–188

[2] Wang Y, Zhao Q. Patchwise tracking via spatio-temporal constraintbased sparse representation and multiple-instance learning-based SVM. In: Proceedings of International Conference on Neural Information Processing. 2015, 264–271

[3] Li K, He F, Chen X. Real-time object tracking via compressive feature selection. Frontiers of Computer Science, 2016, 10(4): 689–701

[4] Wu G, Lu W, Gao G, Zhao C, Liu J. Regional deep learning model for visual tracking. Neurocomputing, 2016, 175: 310–323

[5] Wu Y, Pei M, Yang M, Yuan J, Jia Y. Robust discriminative tracking via landmark-based label propagation. IEEE Transactions on Image Processing, 2015, 24(5): 1510–1523

[6] Wang L, Liu T, Wang G, Chan K L, Yang Q. Video tracking using learned hierarchical features. IEEE Transactions on Image Processing, 2015, 24(4): 1424–1435

[7] Zhang K, Liu Q, Wu Y, Yang M H. Robust visual tracking via convolutional networks without training. IEEE Transactions on Image Processing, 2016, 25(4): 1779–1792

[8] Xu C, Tao W, Meng Z, Feng Z. Robust visual tracking via online multiple instance learning with fisher information. Pattern Recognition, 2015, 48(12): 3917–3926

[9] Wang G, Qin X, Zhong F, Liu Y, Li H, Peng Q, Yang M. Visual tracking via sparse and local linear coding. IEEE Transactions on Image Processing, 2015, 24(11): 3796–3809

[10] Sun X, Yao H, Zhang S, Li D. Non-rigid object contour tracking via a novel supervised level set model. IEEE Transactions on Image Processing, 2015, 24(11): 3386–3399

[11] Sui Y, Zhang S, Zhang L. Robust visual tracking via sparsity-induced subspace learning. IEEE Transactions on Image Processing, 2015, 24(12): 4686–4700

[12] Jang S I, Choi K, Toh K A, Teoh A B J, Kim J. Object tracking based on an online learning network with total error rate minimization. Pattern Recognition, 2015, 48(1): 126–139

[13] Sun J, He F, Chen Y, Chen X. A multiple template approach for robust tracking of fast motion target. Applied Mathematics-A Journal of Chinese Universities, 2016, 31(2): 177–197

[14] Hong-tu H, Du-yan B, Yu-fei Z, Shi-ping M, Shan G, Chang L. Robust visual tracking based on product sparse coding. Pattern Recognition Letters, 2015, 56: 52–59

[15] Chen C, Li S, Qin H, Hao A. Real-time and robust object tracking in video via low-rank coherency analysis in feature space. Pattern Recognition, 2015, 48(9): 2885–2905

[16] Zhang T, Liu S, Ahuja N, Yang M H, Ghanem B. Robust visual tracking via consistent low-rank sparse learning. International Journal of Computer Vision, 2015, 111(2): 171–190

[17] Zhang X, Hu W, Xie N, Bao H, Maybank S. A robust tracking system for low frame rate video. International Journal of Computer Vision, 2015, 115(3): 279–304

[18] Zhou Y, Bai X, Liu W, Latecki L J. Similarity fusion for visual tracking. International Journal of Computer Vision, 2016, 118(3): 337–363

[19] Zhang D, He F, Han S, Zou L, Wu Y, Chen Y. An efficient approach to directly compute the exact Hausdorff distance for 3D point sets. Integrated Computer-Aided Engineering, 2017, 24(3), 261–277

[20] Li X, Shen C, Dick A, Zhang Z M, Zhuang Y. Online metric-weighted linear representations for robust visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(5): 931–950

[21] Zhang T, Liu S, Xu C, Yan S, Ghanem B, Ahuja N, Yang M H. Structural sparse tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015, 150–158

[22] Li K, He F, Yu H P. Robust visual tracking based on convolutional features with illumination and occlusion handling. Journal of Computer Science and Technology, 2018, 33(1): 223–236

[23] Liu T,Wang G, Yang Q. Real-time part-based visual tracking via adaptive correlation filters. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2015, 4902–4912

[24] Chen Y L, He F Z, Wu Y Q, Hou N. A local start search algorithm to compute exact Hausdorff distance for arbitrary point sets. Pattern Recognition, 2017, 67: 139–148

[25] Zhang Z, Hong Wong K. Pyramid-based visual tracking using sparsity represented mean transform. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014, 1226–1233

[26] Zhang T, Jia K, Xu C, Ma Y, Ahuja N. Partial occlusion handling for visual tracking via robust part matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014, 1258–1265

[27] Yu H P, He F, Pan Y. A novel region-based active contour model via local patch similarity measure for image segmentation. Multimedia Tools and Applications, 2018, 77(18): 24097–24119

[28] Danelljan M, Shahbaz Khan F, Felsberg M, Van de Weijer J. Adaptive color attributes for real-time visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014, 1090–1097

[29] Yang M, Pei M T, Wu Y W, Jia Y. Learning online structural appearance model for robust object tracking. Science China Information Sciences, 2015, 58(3): 1–14

[30] Smeulders A W, Chu D M, Cucchiara R, Calderara S, Dehghan A, Shah M. Visual tracking: an experimental survey. IEEE Transactions on Pattern Analysis andMachine Intelligence, 2014, 36(7): 1442–1468

[31] Zhang H, Hu S, Yang G. Video object tracking based on appearance models learning. Journal of Computer Research and Development, 2015, 52(1): 177–190

[32] Cehovin L, Leonardis A, Kristan M. Visual object tracking performance measures revisited. IEEE Transactions on Image Processing, 2016, 25(3): 1261–1274

[33] Wu Y, Lim J, Yang M H. Online object tracking: a benchmark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2411–2418

[34] Ni B, He F, Pan Y, Yuan Z. Using shapes correlation for active contour segmentation of uterine fibroid ultrasound images in computeraided therapy. AppliedMathematics-A Journal of Chinese Universities, 2016, 31(1): 37–52

[35] Yan X, He F, Hou N, Ai H. An efficient particle swarm optimization for large scale hardware/software co-design system. International Journal of Cooperative Information Systems, 2018, 27(1): 1741001

[36] Yu Q, Dinh T B, Medioni G. Online tracking and reacquisition using co-trained generative and discriminative trackers. In: Proceedings of European Conference on Computer Vision. 2008, 678–691

[37] Zhang D, He F, Han S, Li X. Quantitative optimization of interoperability during feature-based data exchange. Integrated Computer-Aided Engineering, 2016, 23(1): 31–51

[38] Kwon J, Lee K M. Visual tracking decomposition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2010, 1269–1276

[39] Ross D A, Lim J, Lin R S, Yang M H. Incremental learning for robust visual tracking. International Journal of Computer Vision, 2008, 77(1): 125–141

[40] Belhumeur P N, Kriegman D J. What is the set of images of an object under all possible illumination conditions? International Journal of Computer Vision, 1998, 28(3): 245–260

[41] Mei X, Ling H. Robust visual tracking using L1 minimization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2009, 1436–1443

[42] Bao C, Wu Y, Ling H, Ji H. Real time robust L1 tracker using accelerated proximal gradient approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1830–1837

[43] Jia X, Lu H, Yang M H. Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1822–1829

[44] Zhong W, Lu H, Yang M H. Robust object tracking via sparsity-based collaborative model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1838–1845

[45] Wang D, Lu H, Yang M H. Online object tracking with sparse prototypes. IEEE Transactions on Image Processing, 2013, 22(1): 314–325

[46] Wang N, Wang J, Yeung D Y. Online robust non-negative dictionary learning for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision. 2013, 657–664

[47] Zhang S, Yao H, Sun X, Lu X. Sparse coding based visual tracking: review and experimental comparison. Pattern Recognition, 2013, 46(7): 1772–1788

[48] Babenko B, Yang M H, Belongie S. Visual tracking with online multiple instance learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2009, 983–990

[49] Wang N, Shi J, Yeung D Y, Jia J. Understanding and diagnosing visual tracking systems. In: Proceedings of the IEEE International Conference on Computer Vision. 2015, 3101–3109

[50] Hare S, Golodetz S, Saffari A, Vineet V, Cheng M M, Hicks S L, Torr P H. Struck: structured output tracking with kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(10): 2096–2109

[51] Zhang K, Zhang L, Yang M. Real-time compressive tracking. In: Proceedings of European Conference on Computer Vision. 2012, 864–877

[52] Kalal Z, Matas J, Mikolajczyk K. PN learning: bootstrapping binary classifiers by structural constraints. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2010, 49–56

[53] Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409–1422

[54] Grabner H, Grabner M, Bischof H. Realtime tracking via on-line boosting. In: Proceedings of British Machine Vision Conference. 2006, 47–56

[55] Arulampalam M S, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 2002, 50(2): 174–188

[56] Li K, He F, Ye H P, Chen X. A correlative classiffiers approach based on particle filter and sample set for tracking occluded target. Applied Mathematics-A Journal of Chinese Universities, 2017, 32(3): 294–312

[57] Levey A, Lindenbaum M. Sequential Karhunen-Loeve basis extraction and its application to images. IEEE Transactions on Image Processing, 2000, 9(8): 1371–1374

[58] Wang D, Lu H C, Yang M H. Least soft-thresold squares tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2371–2378

[59] Wang D, Lu H. Visual tracking via probability continuous outlier model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014, 3478–3485

[60] Jia X, Lu H, Yang M H. Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1822–1829

[61] Dinh T B, Vo N, Medioni G. Context tracker: exploring supporters and distracters in unconstrained environments. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2011, 1177–1184

[62] Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2001, 511–518

[63] Zhou Y, He F, Qiu Y. Optimization of parallel iterated local search algorithms on graphics processing unit. The Journal of Supercomputing, 2016, 72(6): 2394–2416

[64] Zhou Y, He F, Qiu Y. Dynamic strategy based parallel ant colony optimization on GPUs for TSPs. Science China Information Sciences, 2017, 60(6): 068102

[65] Wu Y, He F, Zhang D, Li X. Service-oriented feature-based data exchange for cloud-based design and manufacturing. IEEE Transactions on Services Computing, 2015, 11(2): 341–353

[66] Zhou Y, He F, Hou N. Parallel ant colony optimization on multi-core simdcpus. Future Generation Computer Systems, 2018, 79(2): 473–487

[67] Yan X, He F, Chen Y. A novel hardware/software partitioning method based on position disturbed particle swarm optimization with invasive weed optimization. Journal of Computer Science and Technology, 2017, 32(2): 340–355

[68] Lv X, He F, Cai W. Supporting selective undo of string-wise operations for collaborative editing systems. Future Generation Computer Systems, 2018, 82: 41–62

[69] Lv X, He F, Cai W, Cheng Y. A string-wise CRDT algorithm for smart and large-scale collaborative editing systems. Advanced Engineering Informatics, 2017, 33: 397–409

[70] Zhu H, Nie Y, Yue T, Cao X. The role of prior in image based 3D modeling: a survey. Frontiers of Computer Science, 2017, 11(2): 175–191

[71] Han Y, Jia G. Optimizing product manufacturability in 3D printing. Frontiers of Computer Science, 2017, 11(2): 347–357

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