Real-time tracking using multiple features based on compressive sensing

As traditional tracking algorithm based on compressive sensing can extrack few features and fails to track targets stably in textures and lightings changed, a real-time tracking algorithm using multi-features based on compressive sensing is proposed. The algorithm uses multiple matrixes as the projection matrix of the compressive sensing, and the compressed data as the multiple features to extract the multiple features needed by track. Because the feature stability is different in tracky processing, different update levels are taken to maintain the tracking robustness in varied target conditions. The proposed algorithm is tested with variant video sequences and the results show that the algorithm achieves stable tracking for the target moved or the light changed, and average computing frame rate is 23 frame/s when the target scale is 70 pixel×100 pixel. Obtained results satisfy the requirements of real-time tracking. As compared with the compressive tracking with single kind of feature, the algorithm can track stably under big changed lightings and target textures.