Real-time Visual Tracking Using Compressive Sensing

The l(1) tracker obtains robustness by seeking a sparse representation of the tracking object via l(1) norm minimization. However, the high computational complexity involved in the l(1) tracker may hamper its applications in real-time processing scenarios. Here we propose Real-time Compressive Sensing Tracking (RTCST) by exploiting the signal recovery power of Compressive Sensing (CS). Dimensionality reduction and a customized Orthogonal Matching Pursuit (OMP) algorithm are adopted to accelerate the CS tracking. As a result, our algorithm achieves a real-time speed that is up to 5, 000 times faster than that of the l(1) tracker. Meanwhile, RTCST still produces competitive (sometimes even superior) tracking accuracy compared to the l(1) tracker. Furthermore, for a stationary camera, a refined tracker is designed by integrating a CS-based background model (CSBM) into tracking. This CSBM-equipped tracker, termed RTCST-B, outperforms most state-of-the-art trackers in terms of both accuracy and robustness. Finally, our experimental results on various video sequences, which are verified by a new metric-Tracking Success Probability (TSP), demonstrate the excellence of the proposed algorithms.