Abstract: Compressive tracking algorithm can not select appropriate object futures which will result in drifting or make tracking not accurate when the object is occluded or its appearance changes.To address this problem,this paper proposed a real-time compressive tracking algorithm based on rectangle feature selection.Firstly,generate projection matrixes are generated in an initial phase.And the projection matrixes are used to extract the feature to construct a feature pool.The rectangle feature is used to represent the characteristics of target in the feature pool, and the rectangular features with greater difference from the target characteristics are removed.Finally,the classifier is taken to process candidate samples by Bayes classification and response results to the classifier are taken as tracking results.The experimental results show that the proposed algorithm is about 13% lower than that of compressive tracking.It improves the trac-king accuracy and robustness,and the processing frame rate is 20 frame/s on a 320pixel×240pixel video sequence,which meets the requirements of real-time tracking.

Key words: Compressive sensing,Online rectangle feature selection,Compressive tracking,Feature pool