  The $\ell_1$ tracker obtains robustness by seeking a sparse representation of
the tracking object via $\ell_1$ norm minimization \cite{Xue_ICCV_09_Track}.
However, the high computational complexity involved in the $ \ell_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 $\ell_1$ tracker.
Meanwhile, RTCST still produces competitive (sometimes even superior) tracking
accuracy comparing to the existing $\ell_1$ 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.
