Object tracking based on low-rank sparse learning usually makes the drift phenomenon occur when the target faces severe occlusion and fast motion. In this paper, we propose a novel tracking algorithm via reverse low-rank sparse learning and fractional-order variation regularization. Firstly, we utilize convex low-rank constraint to force the appearance similarity of the candidate particles, so as to prune the irrelevant particles. Secondly, fractional-order variation is introduced to constrain the sparse coefficient difference in the bounded variation space, which allows the difference between consecutive frames to exist, so as to adapt object fast motion. Meanwhile, fractional-order regularization can restrain severe occlusion by considering more adjacent frames information. Thirdly, we employ an inverse sparse representation method to model the relationship between target candidates and target template, which can reduce the computation complexity for online tracking. Finally, an online updating scheme based on alternating iteration is proposed for tracking computation. Experiments on benchmark sequences show that our algorithm outperforms several state-of-the-art methods, especially exhibiting better adaptability for fast motion and severe occlusion.
Discriminative correlation filter (DCF) has achieved promising performance in visual tracking for its high efficiency and high accuracy. However, DCF trackers usually suffer from some challenges, such as boundary effects and appearance changes. In this paper, we propose a novel correlation tracking method via spatial-temporal constraints and structured sparse regularization. Firstly, we introduce the background-aware selection strategy to extract real negative examples, and penalize the filter coefficients close to the boundary locations for spatial protection, both of which can alleviate the boundary effects. Secondly, we restrict the filters with structured sparse regularization to handle the local appearance changes, and exploit temporal consistent constraint on the filters to address the global appearance changes. Finally, we employ the alternative direction method of multipliers to optimize our correlation tracking model. In our optimization framework, we combine grayscale, color names, histogram of orientation gradient with deep features for appearance learning to improve the discrimination. Meanwhile, we penalize spatial constraint and structured sparse regularization alternatively based on occlusion detection to enhance processing efficiency. The qualitative and quantitative experiments are conducted on the OTB dataset. Experimental results demonstrate that the proposed tracker has better performance than other state-of-theart trackers.INDEX TERMS Object tracking, correlation filter, spatial-temporal constraints, structured sparse regularization, deep feature This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
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