In this project, the authors facilitated achieving a common goal in many forensics vision applications, which is to identify and track human objects with distinctive visual features or "tags".
In this paper, the authors made two contributions to this "visual tagging" problem. First, they propose a general framework for camera placement. This framework can measure the performance of any particular camera placement using simulation method. The optimal placement strategy can be obtained by iterative grid-based linear programming. Second, the authors focused on tracking specific, colored tags used in a privacy-protecting visual surveillance network. By building a color classifier for tag detection and using epipolar geometry between multiple cameras for occlusion handling, the proposed system can identify, track, and visually obfuscate individuals whose privacy in the surveillance video needs to be protected. (Publisher abstract provided)