Object tracking with an effective composite similarity

How to achieve a robust performance remains an intractable problem with the various object tracking algorithms, due to some unfavorable factors, e.g., occlusion, appearance change, etc. A robust object-tracking approach is proposed based on a composite similarity measure. Based on the first several frames in a video sequence, an initial dictionary is constructed by searching for the best candidates via the kd-tree method. For a target candidate, a likelihood value is computed by weighting the sparse representation coefficients of local patches obtained via the adaptive structural local sparse appearance (ASLSA) model. In order to alleviate the effect of occlusion, a smoothing operation is designed in computing the likelihood value. Meanwhile, after obtaining the codebook by applying the k-means clustering algorithm on the randomly sampled positive and negative templates, a confidence value is computed, via the sparse collaborative model (SCM), for each of the candidates. Furthermore, a composite similarity measure is devised, which combines the likelihood value and the confidence value for each candidate. Finally, the best candidate is searched for, according to the composite similarity measure. Due to the complementarity of SCM and ASLSA, the proposed algorithm is a more robust approach to object tracking. Experimental results on several challenging sequences demonstrate the effectiveness and feasibility of the proposed method.