Extended compressed tracking via random projection based on MSERs and online LS-SVM learning

Abstract

The compressed tracking algorithm (CT tracker) is a well-known visual tracking method that models a target object׳s appearance through sparse random projection. However, the tracking results are not stable and robust due to the randomness of random projection. To solve this problem, a more stable and robust approach is proposed for visual tracking based on maximally stable extremal regions (MSERs), sparse random projection and online least squares SVM classifier (LS-SVM) learning. To obtain a relatively stable appearance model, the stable connected components of an object based on MSERs in image feature space are extracted. With the fusion of MSERs and sparse random projection, we model adaptive object appearance to adapt the variation of appearance. Additionally, an online closed-form LS-SVM is employed to quickly and robustly predict the target object location in a tracking by detection framework. Experimental results on benchmark sequences show the stability and robustness of the proposed algorithm compared with the existing CT-based trackers and other state-of-the-art trackers.