Object tracking in the presence of occlusions via a camera network

This paper describes a sensor network approach to tracking a single object in the presence of static and moving occluders using a network of cameras. To conserve communication bandwidth and energy, each camera first performs simple local processing to reduce each frame to a scan line. This information is then sent to a cluster head to track a point object. We assume the locations of the static occluders to be known, but only prior statistics on the positions of the moving occluders are available. A noisy perspective camera measurement model is presented, where occlusions are captured through an occlusion indicator function. An auxiliary particle filter that incorporates the occluder information is used to track the object. Using simulations, we investigate (i) the dependency of the tracker performance on the accuracy of the moving occluder priors, (ii) the tradeoff between the number of cameras and the occluder prior accuracy required to achieve a prescribed tracker performance, and...

• Type: Conference
• Year: 2007
• Where: IPSN
• Authors: Ali Ozer Ercan, Abbas El Gamal, Leonidas J. Guibas
• Keywords: Auxiliary Particle filter, IPSN 2007, Noisy Perspective Camera, Occluder Priors, Sensor Networks