Abstract Vision based tracking systems for surveillance and motion capture rely on a set of cameras to sense the environment. The exact placement or configuration of these cameras can have a profound affect on the quality of tracking which is achievable. Although several factors contribute, occlusion due to moving objects within the scene itself is often the dominant source of tracking error. This work introduces a configuration quality metric based on the likelihood of dynamic occlusion. Since the exact geometry of occluders can not be known a priori, we use a probabilistic model of occlusion. This model is extensively evaluated experimentally using hundreds of different camera configurations and found to correlate very closely with the actual probability of feature occlusion.
Chen & Davis REF developed a probabilistic model of occlusion.
An occlusion metric for selecting robust camera configurations
Machine Vision and Applications
Machine Vision and Applications
Computer Science