Anytime learning for the NoSLLiP tracker

We propose an anytime learning for the Sequence of Learned Linear Predictors (SLLiP) tracker. Since the learning might be time consuming for large problems, we present an anytime learning algorithm which, after a very short initialization period, provides a solution with defined precision. As SLLiP tracking requires only a fraction of the processing power of an ordinary PC, the learning can continue in a parallel background thread continuously delivering improved SLLiPs, ie. faster, with lower computational complexity, with the same pre-defined precision. The proposed approach is verified on publicly-available sequences with approximately 12000 ground truthed frames. The learning time is shown to be twenty times smaller than learning based on linear programming proposed in the paper that introduced the SLLiP tracker [TR]. Its robustness and accuracy is similar. Superiority in frame-rate and robustness with respect to the SIFT detector, Lucas-Kanade tracker and Jurie's tracker is also demonstrated.