The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.11 - November (2011 vol.33)
pp: 2259-2272
Xue Mei , University of Maryland, Folsom
Haibin Ling , Temple University, Philadelphia
ABSTRACT
In this paper, we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, noise, and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target in a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an \ell_1-regularized least-squares problem. Then, the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework. Two strategies are used to further improve the tracking performance. First, target templates are dynamically updated to capture appearance changes. Second, nonnegativity constraints are enforced to filter out clutter which negatively resembles tracking targets. We test the proposed approach on numerous sequences involving different types of challenges, including occlusion and variations in illumination, scale, and pose. The proposed approach demonstrates excellent performance in comparison with previously proposed trackers. We also extend the method for simultaneous tracking and recognition by introducing a static template set which stores target images from different classes. The recognition result at each frame is propagated to produce the final result for the whole video. The approach is validated on a vehicle tracking and classification task using outdoor infrared video sequences.
INDEX TERMS
Visual tracking, sparse representation, compressive sensing, simultaneous tracking and recognition, particle filter, \ell_1 minimization.
CITATION
Xue Mei, Haibin Ling, "Robust Visual Tracking and Vehicle Classification via Sparse Representation", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.33, no. 11, pp. 2259-2272, November 2011, doi:10.1109/TPAMI.2011.66
REFERENCES
[1] M. Andriluka, S. Roth, and B. Schiele, "People-Tracking-by-Detection and People-Detection-by-Tracking," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-8, 2008.
[2] S. Avidan, "Ensemble Tracking," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 494-501, 2005.
[3] B. Babenko, M. Yang, and S. Belongie, "Visual Tracking with Online Multiple Instance Learning," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[4] S. Baker and I. Matthews, "Lucas-Kanade 20 Years On: A Unifying Framework," Int'l J. Computer Vision, vol. 56, pp. 221-255, 2004.
[5] M.D. Breitenstein, F. Reichlin, B. Leibe, E. Koller-Meier, L.V. Gool, "Robust Tracking-by-Detection Using a Detector Confidence Particle Filter," Proc. Int'l Conf. Computer Vision, 2009.
[6] M.J. Black and A.D. Jepson, "Eigentracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation," Int'l J. Computer Vision, vol. 26, pp. 63-84, 1998.
[7] Y. Cai, N. Freitas, and J. Little, "Robust Visual Tracking for Multiple Targets," Proc. European Conf. Computer Vision, pp. 107-118, 2006.
[8] E. Candès, J. Romberg, and T. Tao, "Stable Signal Recovery from Incomplete and Inaccurate Measurements," Comm. Pure and Applied Math., vol. 59, no. 8, pp. 1207-1223, 2006.
[9] V. Cevher, A. Sankaranarayanan, M.F. Duarte, D. Reddy, R.G. Baraniuk, and R. Chellappa, "Compressive Sensing for Background Subtraction," Proc. European Conf. Computer Vision, 2008.
[10] R.T. Collins and Y. Liu, "On-Line Selection of Discriminative Tracking Features," Proc. IEEE Int'l Conf. Computer Vision, pp. 346-352, 2003.
[11] D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-Based Object Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564-577, May 2003.
[12] l1_ls: Simple Matlab Solver for l1-Regularized Least Squares Problems, http://www.stanford.edu/~boydl1_ls/, 2011.
[13] D. Donoho, "Compressed Sensing," IEEE Trans. Information Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.
[14] A. Doucet, N. de Freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice. Springer-Verlag, 2001.
[15] G.J. Edwards, C.J. Taylor, and T.F. Cootes, "Improving Identification Performance by Integrating Evidence from Sequences," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 486-491, 1999.
[16] J. Gu, S. Nayar, E. Grinspun, P. Belhumeur, and R. Ramamoorthi, "Compressive Structured Light for Recovering Inhomogeneous Participating Media," Proc. European Conf. Computer Vision, 2008.
[17] G.D. Hager and P.N. Belhumeur, "Efficient Region Tracking with Parametric Models of Geometry and Illumination," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 10, pp. 1025-1039, Oct. 1998.
[18] J. Ho, K.-C. Lee, M.-H. Yang, and D. Kriegman, "Visual Tracking Using Learned Subspaces," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 782-789, 2004.
[19] K. Hotta, "Adaptive Weighting of Local Classifiers by Particle Filters for Robust Tracking," Pattern Recognition, vol. 42, no. 5, pp. 619-628, 2009.
[20] J. Huang, X. Huang, and D. Metaxas, "Learning with Dynamic Group Sparsity," Proc. IEEE Int'l Conf. Computer Vision, 2009.
[21] M. Isard and A. Blake, "Condensation—Conditional Density Propagation for Visual Tracking," Int'l J. Computer Vision, vol. 29, pp. 5-28, 1998.
[22] A.D. Jepson, D.J. Fleet, and T.F. El-Maraghi, "Robust Online Appearance Models for Visual Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1296-1311, Oct. 2003.
[23] Z. Kalal, J. Matas, and K. Mikolajczyk, "P-N Learning: Bootstrapping Binary Classifiers by Structural Constraints," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
[24] T. Kaneko and O. Hori, "Feature Selection for Reliable Tracking Using Template Matching," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 796-802, 2003.
[25] Z. Khan, T. Balch, and F. Dellaert, "A Rao-Blackwellized Particle Filter for EigenTracking," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 980-986, 2004.
[26] S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, "A Method for Large-Scale l1-Regularized Least Squares," IEEE J. Selected Topics in Signal Processing, vol. 1, no. 4, pp. 606-617, 2007.
[27] M. Kristan, S. Kovacic, A. Leonardis, and J. Pers, "A Two-Stage Dynamic Model for Visual Tracking," IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 40, no. 6, pp. 1505-1520, Dec. 2010.
[28] K.-C. Lee and D. Kriegman, "Online Learning of Probabilistic Appearance Manifolds for Video-Based Recognition and Tracking," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 852-859, 2005.
[29] X. Liu and T. Chen, "Video-Based Face Recognition Using Adaptive Hidden Markov Models," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 340-345, 2003.
[30] B. Lucas and T. Kanade, "An Iterative Image Registration Technique with an Application to Stereo Vision," Proc. Int'l Joint Conf. Artificial Intelligence, pp. 674-679, 1981.
[31] J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, "Discriminative Learned Dictionaries for Local Image Analysis," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[32] I. Matthews, T. Ishikawa, and S. Baker, "The Template Update Problem," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 6, pp. 810-815, June 2004.
[33] X. Mei, H. Ling, and D.W. Jacobs, "Sparse Representation of Cast Shadows via $\ell_1$ -Regularized Least Squares," Proc. IEEE Int'l Conf. Computer Vision, 2009.
[34] X. Mei and H. Ling, "Robust Visual Tracking Using $\ell_1$ Minimization," Proc. IEEE Int'l Conf. Computer Vision, 2009.
[35] X. Mei, S.K. Zhou, and H. Wu, "Integrated Detection, Tracking and Recognition for IR Video-Based Vehicle Classification," Proc. IEEE Int'l Conf. Acoustics, Speech and Signal Processing, pp. 745-748, 2006.
[36] J. Mooser, Q. Wang, S. You, and U. Neumann, "Fast Simultaneous Tracking and Recognition Using Incremental Keypoint Matching," Proc. Int'l Symp. 3D Data Processing, Visualization, and Transmission, 2008.
[37] F. Porikli, O. Tuzel, and P. Meer, "Covariance Tracking Using Model Update Based on Lie Algebra," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 728-735, 2006.
[38] D.A. Ross, J. Lim, R. Lin, and M. Yang, "Incremental Learning for Robust Visual Tracking," Int'l J. Computer Vision, vol. 77, pp. 125-141, 2008.
[39] J. Sakagaito and T. Wada, "Nearest First Traversing Graph for Simultaneous Object Tracking and Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-7, 2007.
[40] H. Sidenbladh, M.J. Black, and D.J. Fleet, "Stochastic Tracking of 3D Human Figures Using 2D Image Motion," Proc. European Conf. Computer Vision, vol. 2, pp. 702-718, 2002.
[41] VIVID Database. https://www.sdms.afrl.af.mil/requestdata_ request.php#vivid , 2010.
[42] O. Williams, A. Blake, and R. Cipolla, "Sparse Bayesian Learning for Efficient Visual Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1292-1304, Aug. 2005.
[43] J. Wright, A.Y. Yang, A. Ganesh, S.S. Sastry, and Y. Ma, "Robust Face Recognition via Sparse Representation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210-227, Feb. 2009.
[44] B. Wu and R. Nevatia, "Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet Based Part Detectors," Int'l J. Computer Vision, vol. 75, no. 2, pp. 247-266, 2007.
[45] A. Yilmaz, O. Javed, and M. Shah, "Object Tracking: A Survey," ACM Computing Surveys, vol. 38, no. 4, 2006.
[46] Q. Yu, T.B. Dinh, and G. Medioni, "Online Tracking and Reacquistion Using Co-Trained Generative and Discriminative Trackers," Proc. European Conf. Computer Vision, pp. 678-691, 2008.
[47] S.K. Zhou, R. Chellappa, and B. Moghaddam, "Visual Tracking and Recognition Using Appearance-Adaptive Models in Particle Filters," IEEE Trans. Image Processing, vol. 13, no. 11, pp. 1491-1506, Nov. 2004.
27 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool