Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A Probabilistic Exclusion Principle for Tracking Multiple Objects
International Journal of Computer Vision
Probabilistic Data Association Methods for Tracking Complex Visual Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convex Optimization
Spatio-Temporal Context for Robust Multitarget Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking appearances with occlusions
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Tracking multi-object under occlusion is a challenging task. When occlusion happens, only the visible part of occluded object can provide reliable information for the matching. In conventional algorithms, the deducing of the occlusion relationship is needed to derive the visible part. However deducing the occlusion relationship is difficult. The interdetermined effect between the occlusion relationship and the tracking results will degenerate the tracking performance, and even lead to the tracking failure. In this paper, we propose a novel framework to track multi-object with occlusion handling according to sparse reconstruction. The matching with l1-regularized sparse reconstruction can automatically focus on the visible part of the occluded object, and thus exclude the need of deducing the occlusion relationship. The tracking is simplified into a joint Bayesian inference problem. We compare our algorithm with the state-of-the-art algorithms. The experimental results show the superiority of our algorithm over other competing algorithms.