Real-time Visual Tracking Using Sparse Representation
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The ℓ_1 tracker obtains robustness by seeking a sparse representation of the tracking object via ℓ_1 norm minimization Xue_ICCV_09_Track. However, the high computational complexity involved in the ℓ_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 ℓ_1 tracker. Meanwhile, RTCST still produces competitive (sometimes even superior) tracking accuracy comparing to the existing ℓ_1 tracker. Furthermore, for a stationary camera, a further refined tracker is designed by integrating a CS-based background model (CSBM). This CSBM-equipped tracker coined as RTCST-B, outperforms most state-of-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.
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