Target-surround feature attention model of visual tracking

This paper presents a target-surround feature attention (TSFA) model for constructing attention-based visual tracking algorithm. This model extracts attentive region by distinguishing the color contrast between the interested target and its surround. A preference generator provides online feature transformation to update the target/surround biasing masks that describes the color composition associated with the target and its surround. Output of the TSFA model is a saliency map representing occurrence possibility of the target. Tracker based on the mean shift algorithm is used to lock and locate the target on the saliency map. Experimental results show that visual tacking algorithm with the TSFA model may adapt to noisy images under changing illumination.