Visual object tracking via the local soft cosine similarity

In this paper, we propose a robust visual tracking algorithm based on soft similarity under the Bayesian framework. Firstly, we propose a Local Soft Similarity based on Soft Cosine Measure (L3SCM) that measures the soft similarity between two vectors of features in Vector Space Model (VSM) by taking into account dependencies between these features. Secondly, we model the motion model component of the proposed tracker by using the Bayesian framework, then we apply the L3SCM measure into the observation model component to measure the local similarities between the template of the tracked target and the sampled candidates in incoming frame of a given image sequence. Finally, we integrate a simple scheme to update the target template throughout the tracking process in order to improve the robustness of the proposed tracker. Experimental results on several challenging image sequences illustrate that the proposed method performs better against several state-of-the-art trackers.