Abstract
In this paper, we present a low-rank sparse tracking method which builds upon the particle filtering framework. The proposed method learns the local dense scale-invariant feature transform features corresponding to candidate samples jointly by exploiting the underlying sparse and low-rank constraints. Furthermore, the alternating direction method of multipliers method guarantees the optimization equation can be solved accurately and robustly. We evaluate our proposed tracking method against 9 state-of-the-art trackers on a set of 64 challenging sequences. Experimental results show that the proposed method performs favorably against state-of-the-art trackers in terms of accuracy.
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Adam A, Rivlin E, Shimshoni I (2006) Robust fragments-based tracking using the integral histogram. In: Proceedings of IEEE conference on computer vision and pattern recognition, June, pp 798–805
Avidan S (2005) Ensemble tracking. In: Proceedings of IEEE conference on computer vision and pattern recognition, June, pp 494–501
Babenko B, Yang M-H, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632
Bao C, Wu Y, Ling H, Ji H (2012) Real time robust L1 tracker using accelerated proximal gradient approach. In: IEEE conference on computer vision and pattern recognition (CVPR), Rhode Island
Boyd S, Parikh N, Chu E, Peleato B, Eckstein J (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3(1):1–122
Comaniciu D, Member VR, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–575
Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2010) The PASCAL Visual Object Classes Challenge 2010 (VOC2010) Results
Grabner M, Grabner H, Bischof H (2007) Learning features for tracking. In: IEEE conference on computer vision and pattern recognition, CVPR’07. IEEE, pp 1–8
Hare S, Saffari A, Torr PHS (2011) Struck: structured output tracking with kernels. In: Proceedings of IEEE ICCV, November, pp 263–270
Hare S, Saffari A, Torr PHS (2012) Efficient online structured output learning for keypoint-based object tracking. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1894–1901
Ho HT, Chellappa R (2014) Automatic head pose estimation using randomly projected dense SIFT descriptors, vol 8556, pp 153–156
Hong Z, Mei X, Prokhorov D, Tao D (2013) Tracking via robust multi-task multi-view joint sparse representation. In: ICCV
Isard M, Blake A (1998) CONDENSATION—conditional density propagation for visual tracking. IJCV 29(1):5–28
Jia X, Lu H, Yang M-H (2012) Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of IEEE conference on computer vision and pattern recognition, June, pp 1822–1829
Kalal Z, Matas J, Mikolajczyk K (2010) P-N learning: bootstrapping binary classifiers by structural constraints. In: Proceedings of IEEE conference on computer vision and pattern recognition, June, pp 49–56
Kwon J, Lee KM (2010) Visual tracking decomposition. In: Proceedings of IEEE conference on computer vision and pattern recognition, June, pp 1269–1276
Li X, Hu W, Shen C, Zhang Z, Dick A, Hengel AVD (2013) A survey of appearance models in visual object tracking. ACM Trans Intell Syst Technol 4(4):58
Liu C, Yuen J, Torralba A, Sivic J, Freeman W (2008) SIFT flow: dense correspondence across different scenes. In: Proceedings of ECCV, pp 28–42
Lowe DJ (2004) Distinctive image features from scale-invariant keypoints. IJCV 60(2):91–110
Ma B, Shen J, Liu Y, Hu H, Shao L, Li X (2015) Visual tracking using strong classifier and structural local sparse descriptors. IEEE Trans Multimed 17(10):1818–1828
Ma B, Huang L, Shen J, Shao L, Yang M-H, Porikli F (2016) Visual tracking under motion blur. IEEE Trans Image Process 25(12):5867–5876
Mei X, Ling H (2011) Robust visual tracking and vehicle classification via sparse representation. IEEE Trans Pattern Anal Mach Intell 33(11):2259–2272
Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27(10):1615–1630
Quattoni A, Carreras X, Collins M, Darrell T (2009) An efficient projection for L1, infinity regularization. In: International conference on machine learning, pp 857–864
Ren X, Malik J (2007) Tracking as repeated figure/ground segmentation. In: Proceedings of IEEE conference on computer vision and pattern recognition, June, pp 1–8
Ross D, Lim J, Lin RS, Yang MH (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1):125–141
Wang J, Li J, Yau W, Sung E (2010) Boosting dense SIFT descriptors and shape contexts of face images for gender recognition. In: Proceedings of CVPR, pp 96–102
Wang S, Lu H, Yang F, Yang M-H (2011) Superpixel tracking. In: Proceedings of IEEE international conference on computer vision, November, pp 1323–1330
Wang D, Lu H, Yang M (2013a) Online object tracking with sparse prototypes. IEEE Trans Image Process 22(1):314–325
Wang D, Lu H, Yang M-H (2013b) Least soft-thresold squares tracking. In: CVPR, pp 2371–2378
Wang Y, Hu S, Wu S (2015) Visual tracking based on group sparsity learning. Mach Vis Appl 26(1):127–139
Wright J, Ganesh A, Rao S, Peng Y, Ma Y (2009) Robust principal component analysis: exact recovery of corrupted low-rank matrices via convex optimization, In Advances in neural information processing systems, pp 2080–2088
Xiao Z, Lu H, Wang D (2014) L2-RLS-based object tracking. IEEE Trans Circuits Syst Video Technol 24(8):1301–1309
Yang F, Lu H, Yang M (2014) Robust superpixel tracking. IEEE Trans Image Process 23(4):1639–1651
Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):13–32
Yuan X, Yan S (2010) Visual classification with multi-task joint sparse representation. In: IEEE conference on computer vision and pattern recognition, pp 3493–3500
Zhang K, Song H (2013) Real-time visual tracking via online weighted multiple instance learning. Pattern Recognit 46(1):397–411
Zhang T, Ghanem B, Liu S, Ahuja N (2012a) Robust visual tracking via multi-task sparse learning. In: IEEE conference on computer vision and pattern recognition, pp 1–8
Zhang K, Zhang L, Yang M-H (2012b) Real-time compressive tracking. In: Proceedings of European conference on computer vision, vol 3, pp 864–877, Florence, Italy, October
Zhao L, Li X, Xiao J, Wu F, Zhuang Y (2015) Metric learning driven multi-task structured output optimization for robust keypoint tracking. In: Twenty-ninth AAAI conference on artificial intelligence
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This paper is jointly supported by the National Natural Science Foundation of China (61305016) and Fundamental Research Funds for the Central Universities (Grant No. JUSRP1059).
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Wang, Y., Luo, X., Ding, L. et al. Object tracking via dense SIFT features and low-rank representation. Soft Comput 23, 10173–10186 (2019). https://doi.org/10.1007/s00500-018-3571-5
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DOI: https://doi.org/10.1007/s00500-018-3571-5