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 inter-determined 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. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Li, W., Li, B., Zhang, X., Hu, W., Wang, H., & Luo, G. (2011). Occlusion handling with ℓ1-regularized sparse reconstruction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6495 LNCS, pp. 630–640). https://doi.org/10.1007/978-3-642-19282-1_50
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