Object Tracking via Multi-task Least-Soft-Threshold Squares Regression
YASIN Musa1,2, ZHAO Chun-Xia1
1.School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094 2.School of Mechanical Engineering, Xinjiang University, Urumqi 830046
Abstract：In visual object tracking, model representation is one of the core issues directly affecting tracking efficiency. The object model representation needs an efficient template learning from the online complicated data to adapt to the variations caused by intrinsic or extrinsic factors during the tracking process. In this paper, the detailed descriptions of robust object model representation algorithm is provided, and a novel multi-task least-soft-threshold squares tracking framework (MLST) is proposed. In the proposed scheme, the observation model is treated as a multi-task linear regression problem,and the appearance models of the candidate target under different states are represented by using object templates and additive independently and identically distributed Gaussian-Laplacian noise assumption, so that the tracker is well adopted to some complex scenes and is able to predict the accurate state of the object in every frame. Extensive experiments are carried out to validate that the online learning scheme improves the representation accuracy by exploring some specific properties of the target in every task, and the tracker maintains the best ability to achieve favorable performance. The experimental results show that the proposed algorithm performs favorably against several state-of-the-art tracking algorithms.
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2015, Vol. 28 
