Robust Object Tracking Using Constellation Model with Superpixel

Tracking objects under occlusion or non-rigid deformation poses a major problem: appearance variation of target makes existing bounding rectangle based representation vulnerable to background noise imported during adaptive appearance update. We address the object tracking problem by exploring superpixel based visual information around the target. Instead of representing each object with a single holistic appearance model, we propose to track each target with multiple related parts and model the tracking system as a Dynamic Bayesian Network(DBN). Based on visual features from superpixels, we propose a constellation appearance model with multiple parts which is adaptable to appearance variations. A particle-based approximate inference algorithm over the DBN is proposed for tracking. Experimental results show that the proposed algorithm performs favorably against existing object trackers especially during deformation and occlusion.