Robust Visual Tracking via Sparse Feature Selection and Weight Dictionary Update

Sparse representation-based visual tracking methods do not adapt well to changes in the target and backgrounds, and the sparseness of samples does not guarantee optimality. In this paper, we propose a robust visual tracking algorithm using sparse multi-feature selection and adaptive dictionary update based on weight dictionaries. We exploit the color features and texture features of the learning samples to obtain different discriminative dictionaries based on the label consistent K-SVD algorithm, and use the position information of those samples to assign weights to the dictionaries' base vectors, forming the weight dictionaries. For robust visual tracking, we adopt a novel feature selection strategy that combines the weights of dictionaries' base vectors and reconstruction errors to select the best sample. In addition, we introduce adaptive noise energy thresholds and establish a dictionary updating mechanism based on noise energy analysis, which effectively reduces the error accumulation caused by dictionary updating and enhances the adaptability to target and background changes. Comparison experiments show that the proposed algorithm performs favorably against several state-of-the-art methods.