Robust tracking with adaptive appearance learning and occlusion detection

It is still challenging to design a robust and efficient tracking algorithm in complex scenes. We propose a new object tracking algorithm with adaptive appearance learning and occlusion detection in an efficient self-tuning particle filter framework. The appearance of an object is modeled with a set of weighted and ordered submanifolds, which can guarantee the adaptability when there is fast illumination or pose change. To overcome the occlusion problem, we use the reconstruction error data of the appearance model to extract occlusion region by graph cuts. And the tracking result is improved with feedback of occlusion detection. The motion model is also integrated with adaptability to overcome the abrupt motion problem. To improve the efficiency of particle filter, the number of samples is tuned with respect to the scene conditions. Experimental results demonstrate that our algorithm can achieve great robustness, high accuracy and good efficiency in challenging scenes.

[1]  Neil J. Gordon,et al.  Editors: Sequential Monte Carlo Methods in Practice , 2001 .

[2]  Junseok Kwon,et al.  Visual tracking decomposition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Anton van den Hengel,et al.  Learning Compact Binary Codes for Visual Tracking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Haibin Ling,et al.  Robust visual tracking using ℓ1 minimization , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[5]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Ahmed M. Elgammal,et al.  Learning to track: conceptual manifold map for closed-form tracking , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Junzhou Huang,et al.  Robust tracking using local sparse appearance model and K-selection , 2011, CVPR 2011.

[9]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[10]  Haibin Ling,et al.  Robust Visual Tracking using 1 Minimization , 2009 .

[11]  Ehud Rivlin,et al.  Tracking by Affine Kernel Transformations Using Color and Boundary Cues , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Yuan Yan Tang,et al.  High-Order Distance-Based Multiview Stochastic Learning in Image Classification , 2014, IEEE Transactions on Cybernetics.

[13]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[14]  Meng Wang,et al.  Adaptive Hypergraph Learning and its Application in Image Classification , 2012, IEEE Transactions on Image Processing.

[15]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[16]  Horst Bischof,et al.  Semi-supervised On-Line Boosting for Robust Tracking , 2008, ECCV.

[17]  Horst Bischof,et al.  Real-Time Tracking via On-line Boosting , 2006, BMVC.

[18]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[19]  Luc Van Gool,et al.  Robust tracking-by-detection using a detector confidence particle filter , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[20]  Chunhua Shen,et al.  Real-time visual tracking using compressive sensing , 2011, CVPR 2011.

[21]  Bohyung Han,et al.  Learning occlusion with likelihoods for visual tracking , 2011, 2011 International Conference on Computer Vision.

[22]  Zhibin Hong,et al.  Tracking via Robust Multi-task Multi-view Joint Sparse Representation , 2013, 2013 IEEE International Conference on Computer Vision.

[23]  Kyoung Mu Lee,et al.  Visual tracking via geometric particle filtering on the affine group with optimal importance functions , 2009, CVPR.

[24]  Ming-Hsuan Yang,et al.  Robust Object Tracking with Online Multiple Instance Learning , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Junseok Kwon,et al.  Tracking by Sampling Trackers , 2011, 2011 International Conference on Computer Vision.

[26]  Philip H. S. Torr,et al.  Struck: Structured output tracking with kernels , 2011, ICCV.

[27]  Rama Chellappa,et al.  Visual tracking and recognition using appearance-adaptive models in particle filters , 2004, IEEE Transactions on Image Processing.

[28]  Jun Yu,et al.  Semantic preserving distance metric learning and applications , 2014, Inf. Sci..

[29]  Junzhou Huang,et al.  Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization , 2010, ECCV.

[30]  David J. Kriegman,et al.  Visual tracking and recognition using probabilistic appearance manifolds , 2005, Comput. Vis. Image Underst..

[31]  Ehud Rivlin,et al.  Robust Fragments-based Tracking using the Integral Histogram , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[32]  Vladimir Kolmogorov,et al.  An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision , 2004, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Erik Blasch,et al.  Minimum Error Bounded Efficient L1 Tracker with Occlusion Detection (PREPRINT) , 2011 .

[34]  Lei Zhang,et al.  Real-Time Compressive Tracking , 2012, ECCV.

[35]  Ming-Hsuan Yang,et al.  Incremental Learning for Robust Visual Tracking , 2008, International Journal of Computer Vision.

[36]  Yanning Zhang,et al.  Part-Based Visual Tracking with Online Latent Structural Learning , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  David J. Fleet,et al.  Robust Online Appearance Models for Visual Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Gareth Funka-Lea,et al.  Graph Cuts and Efficient N-D Image Segmentation , 2006, International Journal of Computer Vision.