Optimal camera network configurations for visual tagging

Proper placement of cameras in a distributed smart camera network is an important design problem. Not only does it determine the coverage of the surveillance, but it also has a direct impact on the appearance of objects in the cameras which dictates the performance of all subsequent computer vision tasks. In this paper, we propose a generic camera placement model based on the visibility of objects at different cameras. Our motivation stems from the need of identifying and locating objects with distinctive visual features or "tags." This is a very common goal in computer vision with applications ranging from identifying soccer players by their jersey numbers to locating and recognizing faces of individuals. Our proposed framework places no restriction on the visual classification tasks. It incorporates realistic camera models, self occlusion of tags, and occlusion by other moving objects. It is also flexible enough to handle arbitrary-shaped three-dimensional environments. Using this framework, two novel binary integer programming (BIP) algorithms are proposed to find the optimal camera placement for "visual tagging" and a greedy implementation is developed to cope with the complexity of BIP. Extensive performance analysis is performed using Monte Carlo simulations, virtual environment simulations, and real-world experiments. We also demonstrate the usefulness of visual tagging through robust individual identification and obfuscation across multiple camera views for privacy protection.