Collaboration of spatial and feature attention for visual tracking

Although primates can facilely maintain long-duration tracking of an object without infection of occlusion or other near similar distracters, it remains a challenge for computer vision system. Studies in psychology suggest that the ability of primates to focus selective attention on the spatial properties of an object is necessary to observe object quickly and efficiently while focus selective attention on the feature properties of object is necessary to render it more prominent from the distracters. In this paper, we propose a novel spatial-feature attentional visual tracking (SFAVT) algorithm to encode both. In SFAVT, tracking is treated as an on-line binary classification problem where spatial attention is employed in early selective procedure to construct foreground/background appearance model by identifying image patches with good localization properties, and in late selective procedure to update models by maintaining image patches with good discrimitive motion properties. Meanwhile, feature attention works in mode seeking procedure to help select feature spaces that best separate a target from background. The on-line tuned adaptive appearance models by those selected feature spaces are used to train a classifier for target localization, then. Experiments under various real-world conditions show that this algorithm is able to track an object influenced by dramatic distracters while is of comparable time efficiency with meanshift.