Real-time Visual Tracking Using Sparse Representation

The `1 tracker obtains robustness by seeking a sparse representation of the tracking object via `1 norm minimization [1]. However, the high computational complexity involved in the `1 tracker restricts its further applications in real time processing scenario. Hence we propose a Real Time Compressed Sensing Tracking (RTCST) by exploiting the signal recovery power of Compressed 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 6, 000 times faster than that of the `1 tracker. Meanwhile, RTCST still produces competitive (sometimes even superior) tracking accuracy comparing to the existing `1 tracker. Furthermore, for a stationary camera, a further refined tracker is designed by integrating a CS-based background model (CSBM). This CSBMequipped tracker coined as RTCST-B, outperforms most stateof-the-arts with respect to both accuracy and robustness. Finally, our experimental results on various video sequences, which are verified by a new metric—Tracking Success Probability (TSP), show the excellence of the proposed algorithms.