Robust object tracking guided by top-down spectral analysis visual attention

Object tracking is an important computer vision task, and existing tracking algorithms have achieved great progress. However, several challenging issues still remain to be solved, such as abrupt motion, longtime complete occlusion and target reappearing after moving completely out of the frame. To address these issues, we propose a top-down spectral analysis visual attention guided tracking method. Given an image sequence, top-down saliency maps for each input image are calculated by introducing top down information, which is learned at the beginning of tracking process, to spectral analysis attention model. Then, guided by the calculated saliency maps, target search is performed by local and global search processes and also with a validation process. Experimental results demonstrate the effectiveness of the proposed method.