Mutiple-view object tracking using metadata

In this paper we present a moving object tracking method in a multiple camera tracking system using meta-data. The proposed method acquires image sequences from multiple fixed cameras without a priori camera calibration. The tracking is performed based on object´s robust characteristics, such as motion, shape, color, and etc. Elements of meta-data and the optimal tracking method are selected at each frame. Because object´s characteristics are affected by camera position and environmental condition, tracking method between individual cameras is selected by optimal metadata. Tracking in a single view can be divided into three classes, such as; optical flow-based moving object detector, active shape model (ASM), color correlation based detector. The proposed method matches objects to most similar object color information, by comparing meta-data and ASM. In the experiment, the proposed multi-camera tracking system is show to be robust for non-rigid objects.