Progress in Signals and Telecommunication Engineering Yi Ouyang 5 1 2016 For robustly handling the appearance change of target object and heavy occlusion, a novel super- pixels clique based tracking algorithm is proposed. By two stage adaptive appearance modelling method, we propose the method of learning the target-background appearance framework, which is based on super pixels principle histogram bins cluster method. The process of computing superpixels cliques confidence not only store the location information of the superpixels, the super- pixels cliques recent history and last history also are equally weighted. The first phase of twostage adaptive cliques construct and update algorithm is target template superpixels cliques construct stage. By calculating feature distance between superpixels and cliques center, it is to determine whether a superpixel belongs to the cliques. The second phase for detection and updating stage, through compare superpixels features surrounding region of target in training frame, with cliques, the confidence of cliques can be updated. For the target appearance model adaptive learning, a principle histogram bins clustering method be proposed to adaptive update appearance model, and the computational overhead is small. The object can be tracked under appearance changing and occlusion, by bayesian filtering method with using MAP and the adaptive appearance model. Theoretical analysis and experiments results demonstrate that our method outperforms the sate-of-the-art methods when the target under occlusion and illumination changes dramatically.