Robust Object Tracking via Sparsity-based Collaborative Model

1. Robust Object Tracking via Sparsity-based Collaborative Model In CVPR2012 Wei Zhong, Huchuan Lu and Ming-Hsuan Yang http://ice.dlut.edu.cn/lu/index.html http://faculty.ucmerced.edu/mhyang/index.html

2. ●Introduction ● Related Work and Motivation ● The Proposed Method ● Experimental Results ●Conclusion

3. ● Introduction ■ Applications and Challenging Factors ● Related Work and Motivation ● The Proposed Method ● Experimental Results ●Conclusion

4. Introduction • ■ Applications and Challenging Factors • The goal of object tracking is to estimate the states of the target in image sequences. It plays a critical role in vision applications such as motion analysis, activity recognition, video surveillance and traffic monitoring. • Model-free tracking (i.e., only the initial position of the object is known) is a challenging problem as it is difficult to develop a robust algorithm dealing with large appearance change caused by varying illumination, camera motion, occlusions, pose variation and shape deformation.

5. ●Introduction ● Related Work and Motivation ■Object Tracking with Sparse Representation ■Motivation of This Work ● The Proposed Method ● Experimental Results ●Conclusion

6. Related Work • Liu et al. [1] propose a method which selects a sparse and discriminative set of features to improve tracking efficiency and robustness. One potential problem with this approach is that the number of discriminative features is fixed, which may not be effective for tracking in dynamic and complex scenes. • Liu et al. [2] propose a tracking algorithm based on histograms of local sparse representation. The histogram generation scheme in [2] does not differentiate foreground and background patches, and reduces the discrimination of the method. • Mei and Ling [3] apply sparse representation to visual tracking and deal with occlusionsvia trivial templates.The algorithm is able to deal with occlusion with l1 minimization formulation using trivial templates at the expense of high computational cost. [1] B. Liu, L. Yang, J. Huang, P. Meer, L. Gong, and C. Kulikowski. Robust and fast collaborative tracking with two stage sparse optimization. In ECCV, 2010. [2] B. Liu, J. Huang, L. Yang, and C. Kulikowsk. Robust tracking using local sparse appearance model and k-selection. In CVPR, 2011. [3] X. Mei and H. Ling. Robust visual tracking using l1 minimization. In ICCV, 2009.

7. Motivation • The Motivation of Our Work • We develop a simple yet robust model that makes use of the generative model to account for appearance change and the discriminativeclassifier to effectively separate the foreground target from the background. • Our approach exploits both the strength of holistictemplates to distinguish the target from the background, and the effectiveness of local patches in handling partial occlusion. • In order to capture appearance variations as well as reduce tracking drifts, we propose a method that takes occlusions into consideration for updating appearance model.

8. ●Introduction ● Related Work and Motivation ● The Proposed Method ■ Sparsity-based Discriminative Classifier (SDC) ■ Sparsity-based Generative Model (SGM) ■ Collaborative Model ● Experimental Results ●Conclusion

9. Sparsity-based Discriminative Classifier (SDC) • Template Generation This facilities better object localization as samples containing only partial appearance of the target are treated as the negative samples and their confidence values are restricted to be small.

10. Sparsity-based Discriminative Classifier (SDC) • Feature Selection • The gray-scale feature space is rich yet redundant. With Equation (1), we exact sparse and determinative features that can better distinguish foreground and background. (1)

11. Sparsity-based Discriminative Classifier (SDC) • Confidence Measure (2)

12. Sparsity-based Generative Model (SGM) • Histogram Generation • We use overlapped sliding windows on the normalized images to obtain M patches. • The sparse coefficient vector β of eachpatch is computed byEquation (3). • (3) • In this work, the sparse coefficient vector β of each patch is concatenated to form a histogram by Equation (4). (4)

13. Sparsity-based Generative Model (SGM) • Occlusion Handling • In order to deal with occlusions, we modify the constructed histogram to exclude the occluded patches when describing the target object. (5) • The patch with large reconstruction error is regarded as occlusion and the corresponding sparse coefficient vector is set to be zero. (6)

14. Sparsity-based Generative Model (SGM) • Similarity Function • We use the histogram intersection function to compute the similarity of histograms between the candidate and the template due to its effectiveness by Equation (7). (7)

15. Collaborative Model • We propose a collaborative model using SDC and SGM within the particle filter framework , and the tracking result is the candidate with the highest probability. • The generative model is effective to account for appearance change; • The discriminative classifier is effective to separate the foreground target from the background; • Our method exploits the collatborative strength of both schemes using Equation (8). (8)

16. ●Introduction ● Related Work and Motivation ● The Proposed Method ● Experimental Results ■Qualitative Evaluation ■Quantitative Evaluation ●Conclusion

17. Experimental Results- Qualitative Evaluation Demo: Heavy Occlusion Motion Blur Rotation Illumination Change Cluttered Background

18. Experimental Results- Qualitative Evaluation

19. Experimental Results- Quantitative Evaluation

20. Experimental Results- Quantitative Evaluation

21. ●Introduction ● Related Work and Motivation ● The Proposed Method ● Experimental Results ●Conclusion

22. Conclusion • In this paper, we propose an effective and robust tracking method based on the collaboration of generative and discriminative models. • The SDC module can effectively deal with cluttered and complex background. • The SGM module enables our tracker to better handle heavy occlusion. • Experiments demonstrate the robustness of our tracker.

23. Thank You!