Robust Visual Tracking via Basis Matching

Most existing tracking approaches are based on either the tracking by detection framework or the tracking by matching framework. The former needs to learn a discriminative classifier using positive and negative samples, which will cause tracking drift due to unreliable samples. The latter usually performs tracking by matching local interest points between a target candidate and the tracked target, which is not robust to target appearance changes over time. In this paper, we propose a novel tracking by matching framework for robust tracking based on basis matching rather than point matching. In particular, we learn the target model from target images using a set of Gabor basis functions, which have large responses on the corresponding spatial positions after a max pooling. During tracking, a target candidate is evaluated by computing the responses of the Gabor basis functions on their corresponding spatial positions. The experimental results on a set of challenging sequences validate that the performance of the proposed tracking method outperforms those of several state-of-The-Art methods.