Correlation Tracking via Spatial-Temporal Constraints and Structured Sparse Regularization

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-the-art trackers