Multicamera People Tracking Using a Locus-based Probabilistic Occupancy Map

Tao Hu, Sinan Mutlu, Oswald Lanz

Abstract: We propose a novel people detection method using a Locus-based Probabilistic Occupancy Map (LPOM). Given the calibration data and the motion edges extracted from all views, the method is able to compute the probabilistic occupancy map for the targets in the scene. We integrate the algorithm into a Bayesian-based tracker and do experiments with challenging video sequences. Experimental results demonstrate the robustness and high-precision of the tracker when tracking multiple people in the presence of clutters and occlusions.