Robust visual tracking using dynamic feature weighting based on multiple dictionary learning

Abstract

Using multiple features in appearance modeling has shown to be effective for visual tracking. In this paper, we dynamically measured the importance of different features and proposed a robust tracker with the weighted features. By doing this, the dictionaries are improved in both reconstructive and discriminative way. We extracted multiple features of the target, and obtained multiple sparse representations, which plays an essential role in the classification issue. After learning independent dictionaries for each feature, we then implement weights to each feature dynamically, with which we select the best candidate by a weighted joint decision measure. Experiments have shown that our method outperforms several recently proposed trackers.