A CPU-GPU hybrid people counting system for real-world airport scenarios using arbitrary oblique view cameras

This work
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presents a real-time hybrid CPU-GPU implementation of a practical people counting system, developed for real-world airport scenarios and using the existing airport single cameras. The cameras are characterized by low quality images and are installed in arbitrary oblique viewing angles and heights relative to the ground plane. The scenes are characterized by large field of view, large scale variations of people size, high clutter, and in particular severe occlusions. In addition, people tend to remain long at rest while queuing. Furthermore, real-time performance is required and no elaborate camera calibration is feasible. Our system is based on the fusion of two approaches. The first one is holistic, namely a texture based classification. The second approach utilizes the fast directional Chamfer matching algorithm with variable size ellipse templates to detect heads. Using a probabilistic multi-class SVM classifier for both approaches, the output of the 2 classifier is further fused, yielding a unified prediction.