Abstract
We propose a new tracking method based on a group sparsity learning model. Previous work on sparsity tracking rely on a single sparse model to characterize the templates of tracking targets, which is hard to express complex tracking scenes. In this work, we utilize a superposition of multiple simpler sparse models to capture the structural information across templates. More specifically, our tracking method is formulated within particle filter framework and the particle representations are decomposed into two sparsity norms: a \(l_{1,\infty }\) norm and a \(l_{1,2}\) norm, capturing the common and different information across the templates, respectively. To efficiently implement the proposed tracker, we adapt the alternating direction method of multipliers to solve the formulated two-norm optimization problem. The proposed tracking method is compared with seven state-of-the-art trackers using 16 publicly available and challenging video sequences due to appearance changes, heavy occlusions, and pose variations. Experiment results show that our tracker outperforms the five other tracking methods.
Similar content being viewed by others
References
Chen, S., Zou, B., Li, L.: A novel particle filter with implicit dynamic model for irregular motion tracking. Mach. Vis. Appl. 24(7), 1487–1499 (2013)
Lin, Y., Yu, Q., Medioni, G.: Efficient detection and tracking of moving objects in geo-coordinates. Mach. Vis. Appl. 22(3), 505–520 (2011)
Mei, X., Ling, H.: Robust visual tracking and vehicle classification via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 33(11), 2259–2272 (2011)
Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust L1 tracker using accelerated proximal gradient approach. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Rhode Island (2012)
Mei, X., Ling, H., Wu, Y., Blasch, E., Bai, L.: Efficient minimum error bounded particle resampling L1 tracker with occlusion detection. IEEE Trans. Image Process. 22(7), 2661–2675 (2013)
Jia, X., Lu, H., Yang, M.-H.: Visual tracking via adaptive structural local sparse appearance model. In: CVPR, pp. 1822–1829 (2012)
Chen, X., Pan, W., Kwok, J., Carbonell, J.: Accelerated gradient method for multi-task sparse learning problem. In: IEEE International Conference on Data Mining, pp. 746–751 (2009)
Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Robust visual tracking via multi-task sparse learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2012)
Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 13–32 (2006)
Smeulder, A.W.M., Chu, D.M., Cucchiara, R., Calderara, S., Deghghan, A., Shah, M.: Visual tracking: an experimental survey. IEEE Trans. Pattern Anal. Mach. Intell. (2013)
Li, X., Hu, W., Shen, C., et al.: A survey of appearance models in visual object tracking. ACM Trans. Intell. Syst. Technol. 4(4), 58 (2013)
Babenko, B., Yang, M.-H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1619–1632 (2011)
Zhang, K., Zhang, L., Yang, M.-H.: Real-time compressive tracking. In: Proceedings of European Conference on Computer Vision, vol. 3, pp. 864–877. Florence, Italy, October (2012)
Zhang, K., Zhang, L., Yang, M.-H.: Real-time object tracking via online discriminative feature selection. IEEE Trans. Image Process. 22(12), 4664–4677 (2013)
Grabner, H., Bischof, H.: On-line boosting and vision. In: CVPR (2006)
Ross, D., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1), 125–141 (2008)
Wang, D., Huchuan, L., Yang, M.-H.: Online object tracking with sparse prototypes. IEEE Trans. Image Process. 22(1), 314–325 (2013)
Wu, Y., Shen, B., Ling, H.: Visual tracking via online non-negative matrix factorization. IEEE Trans. Circuits Syst. Video Technol. (in press)
Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409–1422 (2011)
Comaniciu, D., Member, V.R., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–575 (2003)
Liu, B., Huang, J., Yang, L., Kulikowsk, C.: Robust tracking using local sparse appearance model and k-selection. In: CVPR (2011)
Hong, Z., Mei, X., Prokhorov, D., Tao, D.: Tracking via robust multi-task multi-view joint sparse representation. In: ICCV, pp. 649–656 (2013)
Wang, D., Lu, H., Yang, M.-H.: Least soft-threshold squares tracking. In: CVPR, pp. 2371–2378 (2013)
Dinh, T.B., Medioni, G.G.: Co-training framework of generative and discriminative trackers with partial occlusion handling. In: Proceedings of IEEE Workshop on Applications of Computer Vision, pp. 642–649 (2011)
Zhong, W., Lu, H., Yang, M.-H.: Robust object tracking via sparsity-based collaborative model. In: CVPR, pp. 1838–1845 (2012)
Yuan, X., Yan, S.: Visual classification with multi-task joint sparse representation. In: IEEE Conference on Computer Vision and Pattern Recognition (pp. 3493–3500) (2010)
Quattoni, A., Carreras, X., Collins, M., Darrell, T.: An efficient projection for l 1, infinity regularization. In: International Conference on Machine Learning, pp. 857–864 (2009)
Isard, M., Blake, A.: Condensation-conditional density propagation for visual tracking. IJCV 29(1), 5–28 (1998)
Obozinski, G., Taskar, B., Jordan, M.: Joint covariate selection for grouped classification. Technical Report 743, Department of Statistics, University of California Berkeley (2007)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 511–518 (2001)
Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Low-rank sparse learning for robust visual tracking. In: Computer Vision-ECCV, pp. 470–484. Springer, Berlin (2012)
Kwon, J., Lee, K.: Visual tracking decomposition. In: CVPR, pp. 1269–1276 (2010)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J Comput. Vis. 88(2), 303–338 (2010)
Gong, P., Ye, J., Zhang, C.: Robust multi-task feature learning. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 895–903. ACM (2012)
Acknowledgments
This work was jointly supported by the National Natural Science Foundation of China No. 61374161 and No. 61074106. Support of Dr. Shandong Wu was partially provided by the Competitive Medical Research Fund of the University of Pittsburgh Medical Center (UPMC) Health System.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, Y., Hu, S. & Wu, S. Visual tracking based on group sparsity learning. Machine Vision and Applications 26, 127–139 (2015). https://doi.org/10.1007/s00138-014-0654-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00138-014-0654-x