Robust Tracking via Locally Structured Representation

摘要

Representation method is critical to visual tracking. A robust representation describes the target accurately, leading to good tracking performance. In this work, a novel representation is proposed, which is designed to be simultaneously low-rank and joint sparse for the local patches within a target region. In this representation, the subspace structure is exploited by the low-rank constraint to reflect the global information of all the patches, and the sparsity structure is captured by the joint sparsity restriction to describe the locally intimate relationship between the neighboring patches. Importantly, to make the representation computationally applicable to visual tracking, a novel fast algorithm based on greedy strategy is proposed, and the performance analysis of this algorithm is also presented. Thus, the tracking in this work is formulated as a locally low-rank and joint sparse matching problem within particle filtering framework. A large number of experimental results show that the tracking drift problem is effectively alleviated in various challenging situations by using the proposed representation method. Both qualitative and quantitative evaluations demonstrate that the proposed tracker performs favorably against many other state-of-the-art trackers. Benefitting from the good adaptive capability of the representation, all the parameters of the proposed tracking algorithm are fixed in all the experiments.