Object Tracking across Non-overlapping Cameras Using Adaptive Models

In this paper, we propose a novel approach to track multiple objects across non-overlapping cameras, which aims at giving each object a unique label during its appearance in the whole multi-camera system. We formulate the problem of the multiclass object recognition as a binary classification problem based on an AdaBoost classifier. As the illumination variance, viewpoint changes, and camera characteristic changes vary with camera pairs, appearance changes of objects across different camera pairs generally follow different patterns. Based on this fact, we use a categorical variable indicating the entry/exit cameras as a feature to deal with different patterns of appearance changes across cameras. For each labeled object, an adaptive model describing the intraclass similarity is computed and integrated into a sequence based matching framework, depending on which the final matching decisions are made. Multiple experiments are performed on different datasets. Experimental results demonstrate the effectiveness of the proposed method.