Multiple-view object tracking using metadata

Abstract

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.