cvpr12_wei-zhong目标跟踪.ppt

We sample N_p images within the red bounding box. We sample N_n images in the annular region between the red and the blue bounding box. * The matrix A is composed of N_p positive templates and N_n negative templates. Each element of the vector p represents the property of each template in the training set A, i.e., +1 for positive templates and -1 for negative templates. The nonzero elements of vector s correspond to discriminative features selected from the original feature space. * A candidate with smaller reconstruction error using the foreground template set indicates it is more likely to be a target object, and vice versa. Thus, we formulate the confidence value as Equation (2). * * * * * * * * * * * * Robust Object Tracking via Sparsity-based Collaborative Model Wei Zhong, Huchuan Lu and Ming-Hsuan Yang /lu/index.html /mhyang/index.html In CVPR2012 ●Introduction ● Related Work and Motivation ● The Proposed Method ● Experimental Results ●Conclusion ● Introduction ■ Applications and Challenging Factors ● Related Work and Motivation ● The Proposed Method ● Experimental Results ●Conclusion 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. ●Introduction ● Related Work and Motivation ■Object Tracking with Sparse Representation ■Motivation of This Work ● The Proposed Method ● Experimental Results ●Conclusion Related Work Liu et al. [1] propose a method which selects a sparse and discriminative set of features to improve tracking efficiency an