An Efficient Misalignment Method for Visual Tracking Based on Sparse Representation

Shan JIANG  Cheng HAN  Xiaoqiang DI  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.8   pp.2123-2131
Publication Date: 2018/08/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDP7052
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
Keyword: 
visual tracking,  sparse representation,  2D transformation,  template update,  

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Summary: 
Sparse representation has been widely applied to visual tracking for several years. In the sparse representation framework, tracking problem is transferred into solving an L1 minimization issue. However, during the tracking procedure, the appearance of target was affected by external environment. Therefore, we proposed a robust tracking algorithm based on the traditional sparse representation jointly particle filter framework. First, we obtained the observation image set from particle filter. Furthermore, we introduced a 2D transformation on the observation image set, which enables the tracking target candidates set more robust to handle misalignment problem in complex scene. Moreover, we adopt the occlusion detection mechanism before template updating, reducing the drift problem effectively. Experimental evaluations on five public challenging sequences, which exhibit occlusions, illuminating variations, scale changes, motion blur, and our tracker demonstrate accuracy and robustness in comparisons with the state-of-the-arts.