Visual tracking using convolutional features with sparse coding

Visual object tracking has become one of the most active research topics in computer vision, and it has been applied in several commercial applications. Several visual trackers have been presented in the last two decades. They target different tracking objectives. Object tracking from a real-time video is a challenging problem. Therefore, a robust tracker is required to consider many aspects of videos such as camera motion, occlusion, illumination effect, clutter, and similar appearance. In this paper, we propose an efficient object tracking algorithm that adaptively represents the object appearance using CNN-based features. A sparse measurement matrix is proposed to extract the compressed features for the appearance model without sacrificing the performance. We compress sample images of the foreground object and the background by the sparse matrix. When re-detection is needed, the tracking algorithm conducts an SVM classifier on the extracted features with online update in the compressed domain. A search strategy is proposed to reduce the computational burden in the detection step. Extensive simulations with a challenging video dataset demonstrate that the proposed tracking algorithm provides real-time tracking, while delivering substantially better tracking performance than those of the state-of-the-art techniques in terms of robustness, accuracy, and efficiency.