A real?time target tracking method based on compressive sense is investigated. Combining the multiply?features and compressive sense target tracking together, the proposed method introduces a random measurement matrix for detecting in features extraction. Based on the boosting?based frame in tracking, the accuracy of confidence interval estimation is improved by using the identities both of the positive and negative bags of multiply?features. With the proposed method, the chosen target can be tracked on line. Experimental results show that this method can achieve higher efficiency and accuracy in cases such as object moving, pose changing and occlusion. Compared with traditional single feature based target tracking methods, the complementarity of two different features extracted from the proposed method can make the tracking process more robust with a stable and real?time performance.