Selective object and context tracking

Robust appearance model is significantly important to state-of-the-art trackers. However, such trackers highly rely on the reliability of foreground appearance model. When the foreground is seriously occluded or the scene contains multiple objects with similar appearance, such foundation is destroyed. To extend the ability of trackers to handle these difficulties, we propose selective object and context tracking to locate the target according to the reliability of the foreground appearance model which is determined by two measures about whether the target is occluded or surrounded by similar objects. Extensive experiments show that our method achieves better performance than state-of-the-art trackers on VOT TIR-2015 dataset and is able to track the target even when the foreground appearance is completely unreliable.