Locating People in Images by Optimal Cue Integration

This paper describes an approach to segment and locate people in crowded scenarios with application to a surveillance system for airport dependencies. To obtain robust operation, the system analyzes a variety of visual cues –color, motion and shape– and integrates them optimally. A general method for automatic inference of optimal cue integration rules is presented. This schema, based on supervised training on video sequences, avoids the need of explicitly formulate combination rules based on a-priori constraints. The performance of the system is at least as good as classical fusing strategies like those based on voting, because the optimized decision engine implicitly includes these and other strategies.