Visual Attention Model Based Object Tracking

A biological visual attention based object tracking algorithm is proposed. This algorithm combines the top-down, task dependent attention and bottom-up, stimulus driven attention. The image is first decomposed into different feature maps according to the bottom-up attention model. Then with the assumption that object region attracts more attention than background, logistic regression is employed to tune the feature maps, which enhances the object features that are different from background while inhibits the background feature. In this way the saliency map is computed and the object location can be predicted using an efficient search strategy in the saliency map. Experiments show the robustness of the algorithm in object tracking. Moreover the saliency map can be integrated into other object tracking methods as a prior to increase the robustness and efficiency of tracking.