The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.12 - Dec. (2013 vol.35)
pp: 2968-2981
Baiyang Liu , Dept. of Comput. Sci., Rutgers, State Univ. of New Jersey, Piscataway, NJ, USA
Junzhou Huang , Dept. of Comput. Sci. & Eng., Univ. of Arlington, Arlington, TX, USA
Casimir Kulikowski , Dept. of Comput. Sci., Rutgers, State Univ. of New Jersey, Piscataway, NJ, USA
Lin Yang , Dept. of Biostat., Univ. of Kentucky, Lexington, KY, USA
ABSTRACT
Online learned tracking is widely used for its adaptive ability to handle appearance changes. However, it introduces potential drifting problems due to the accumulation of errors during the self-updating, especially for occluded scenarios. The recent literature demonstrates that appropriate combinations of trackers can help balance the stability and flexibility requirements. We have developed a robust tracking algorithm using a local sparse appearance model (SPT) and K-Selection. A static sparse dictionary and a dynamically updated online dictionary basis distribution are used to model the target appearance. A novel sparse representation-based voting map and a sparse constraint regularized mean shift are proposed to track the object robustly. Besides these contributions, we also introduce a new selection-based dictionary learning algorithm with a locally constrained sparse representation, called K-Selection. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than alternatives reported in the recent literature.
INDEX TERMS
Visualization, Target tracking, Histograms, Adaptation models, Heuristic algorithms, Encoding,dictionary learning, Sparse representation, tracking, K-selection, appearance model
CITATION
Baiyang Liu, Junzhou Huang, Casimir Kulikowski, Lin Yang, "Robust Visual Tracking Using Local Sparse Appearance Model and K-Selection", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 12, pp. 2968-2981, Dec. 2013, doi:10.1109/TPAMI.2012.215
REFERENCES
[1] A. Adam, E. Rivlin, and I. Shimshoni, "Robust Fragments-Based Tracking Using the Integral Histogram," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 798-805, 2006.
[2] M. Aharon, M. Elad, and A. Bruckstein, "K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation," IEEE Trans. Signal Processing, vol. 54, no. 11, pp. 4311-4322, Nov. 2006.
[3] S. Avidan, "Ensemble Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 2, pp. 261-271, Feb. 2007.
[4] B. Babenko, M.H. Yang, and S. Belongie, "Visual Tracking with Online Multiple Instance Learning," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[5] 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, no. 1, pp. 329-342, 1998.
[6] M.D. Breitenstein, F. Reichlin, B. Leibe, E. Koller-Meier, and L.V. Gool, "Robust Tracking-by-Detection Using a Detector Confidence Particle Filter," Proc. IEEE Int'l Conf. Computer Vision, 2009.
[7] 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, pp. 155-168, 2008.
[8] D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-Based Object Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564-575, May 2003.
[9] W. Dai and O. Milenkovic, "Subspace Pursuit for Compressive Sensing: Closing the Gap between Performance and Complexity," CoRR, abs/0803.0811, 2008.
[10] K.K. Delgado, J.F. Murray, B.D. Rao, K. Engan, T.W. Lee, and T.J. Sejnowski, "Dictionary Learning Algorithms for Sparse Representation," Neural Computation, vol. 15, no. 2, pp. 349-396, 2003.
[11] H. Grabner and H. Bischof, "On-Line Boosting and Vision," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 260-267, 2006.
[12] H. Grabner, L.C., and H. Bischof, "Semi-Supervised On-Line Boosting for Robust Tracking," Proc. European Conf. Computer Vision, pp. 234-247, 2008.
[13] J. Gu, S.K. Nayar, E. Grinspun, P.N. Belhumeur, and R. Ramamoorthi, "Compressive Structured Light for Recovering Inhomogeneous Participating Media," Proc. European Conf. Computer Vision, pp. 845-858, 2008.
[14] R. Hess and A. Fern, "Discriminatively Trained Particle Filters for Complex Multi-Object Tracking," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[15] J. Huang, X. Huang, and D. Metaxas, "Learning with Dynamic Group Sparsity," Proc. IEEE Int'l Conf. Computer Vision, pp. 64-71, 2009.
[16] 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.
[17] I. Leichter, M. Lindenbaum, and E. Rivlin, "A Probabilistic Framework for Combining Tracking Algorithms," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 445-451, 2004.
[18] B. Liu, J. Huang, L. Yang, and C. Kulikowski, "Robust Tracking Using Local Sparse Appearance model and K-Selection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2011.
[19] B. Liu, L. Yang, J. Huang, P. Meer, L. Gong, and C. Kulikowski, "Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization," Proc. European Conf. Computer Vision, vol. 6314, pp. 624-637, 2010.
[20] J. Mairal, F. Bach, J. Ponce, and G. Sapiro, "Online Dictionary Learning for Sparse Coding," Proc. IEEE Int'l Conf. Machine Learning, vol. 126, pp. 1-8, 2009.
[21] 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.
[22] I. Matthews and S. Baker, "Active Appearance Models Revisited," Int'l J. Computer Vision, vol. 60, no. 2, pp. 135-164, 2004.
[23] L. 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.
[24] X. Mei and H. Ling, "Robust Visual Tracking Using $l_1$ Minimization," Proc. IEEE Int'l Conf. Computer Vision, pp. 1436-1443, 2009.
[25] S.K.M. Kim and V. Pavlovic, "Face Tracking and Recognition with Visual Constraints in Real-World Videos," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[26] 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.
[27] D. Ross, J. Lim, R.S. Lin, and M.H. Yang, "Incremental Learning for Robust Visual Tracking," Int'l J. Computer Vision, vol. 77, no. 1 pp. 125-141, 2008.
[28] J. Santner, C. Leistner, A. Saffari, T. Pock, and H. Bischof, "PROST: Parallel Robust Online Simple Tracking," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
[29] B. Stenger, T. Woodley, and R. Cipolla, "Learning to Track with Multiple Observers," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 2647-2654, 2009.
[30] J.A.T. Anna and C. Gilbert, "Signal Recovery from Random Measurements via Orthogonal Matching Pursuit," IEEE Trans. Information Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007.
[31] J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, "Locality-Constrained Linear Coding for Image Classification," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
[32] 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.
[33] M. Xue, S.K. Zhou, and F. Porikli, "Probabilistic Visual Tracking via Robust Template Matching and Incremental Subspace Update," Proc. IEEE Int'l Conf. Multimedia & Expo, pp. 1818-1821, 2007.
[34] L. Yang, B. Georgescu, Y. Zheng, P. Meer, and D. Comaniciu, "3D Ultrasound Tracking of the Left Ventricles Using One-Step Forward Prediction and Data Fusion of Collaborative Trackers," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[35] L. Yang, B. Georgescu, Y. Zheng, W. Yang, P. Meer, and D. Comaniciu, "Prediction Based Collaborative Trackers (PCT): A Robust and Accurate Approach toward 3D Medical Object Tracking," IEEE Trans. Medical Imaging, vol. 30, no. 11, pp. 1921-1932, Nov. 2011.
[36] M. Yang, Y. Wu, and G. Hua, "Context-Aware Visual Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 7 pp. 1195-1209, July 2009.
[37] A. Yilmaz, O. Javed, and M. Shah, "Object Tracking: A Survey," ACM Computing Surveys, vol. 38, no. 4, pp. 13-32, 2006.
[38] Q. Yu, T.B. Dinh, and G. Medioni, "Online Tracking and Reacquisition Using Co-Trained Generative and Discriminative Trackers," Proc. European Conf. Computer Vision, vol. 5303, pp. 678-691, 2008.
[39] 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.
60 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool