Visual tracking algorithm by harmony search and co-training learning based on multi-cues

To Improve the robust performance of visual tracking in various kind of scene, a novel method by harmony search and co-inference learning based on multi-cues was proposed. The candidate state was achieved by the harmony search and co-inference learning. Then the harmony memory vector corresponded the biggest fitness function was chosen as the state vector. Compared with the harmony search visual tracking with the color cue and the multi-cue integrating with multiply fusion strategy, our algorithm has a better robustness in complex environments such illumination change seriously and motion blur.