在线低秩表示的目标跟踪算法

doi:10.3969/j.issn.1001-2400.2016.05.018

Abstract

Abstract:

Object tracking is an active research topic in computer vision. The traditional tracking methods based on the generative model are sensitive to noise and occlusion, which leads to the failure of tracking results. In order to solve this problem, the tracking results of the first few frames are used as the observation matrix, and the low rank features of the observation model are solved by the the RPCA model. When the new video streams come, a new incremental RPCA is proposed to compute the new observation matrix by the augmented Lagrangian algorithm. The tracking model is established in the Bayesian framework, and the dictionary matrix is updated with the low rank feature. We have tested the proposed algorithm and six state-of-the-art approaches on eight publicly available sequences. Experimental results show that the proposed method has a lower pixel center position error and a higher overlap ratio.

Key words: object tracking, low rank feature, RPCA model, dictionary matrix

WANG Haijun;GE Hongjuan;ZHANG Shengyan. Object tracking via online low rank representation[J].Journal of Xidian University, 2016, 43(5): 98-104.

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Object tracking via online low rank representation

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