Visual object tracking via coefficients constrained exclusive group LASSO

Discriminative methods have been widely applied to construct the appearance model for visual tracking. Most existing methods incorporate online updating strategy to adapt to the appearance variations of targets. The focus of online updating for discriminative methods is to select the positive samples emerged in past frames to represent the appearances. However, the appearances of positive samples might be very dissimilar to each other; traditional online updating strategies easily overfit on some appearances and neglect the others. To address this problem, we propose an effective method to learn a discriminative template, which maintains the multiple appearances information of targets in the long-term variations. Our method is based on the obvious observation that the target appearances vary very little in a certain number of successive video frames. Therefore, we can use a few instances to represent the appearances in the scope of the successive video frames. We propose exclusive group sparse to describe the observation and provide a novel algorithm, called coefficients constrained exclusive group LASSO, to solve it in a single objective function. The experimental results on CVPR2013 benchmark datasets demonstrate that our approach achieves promising performance.