Camera Placement Considering Occlusion for Robust Motion Capture

In multi-camera tracking systems, camera placement can have a significant impact on the overall performance. In feature-based motion capture systems, degradation can come from two major sources, low image resolution and target occlusion. In order to achieve better tracking and automate the camera placement process, a quantitative metric to evaluate the quality of multi-camera configurations is needed. We propose a quality metric that estimates the error caused by both image resolution and occlusion.. It includes a probabilistic occlusion model that reflects the dynamic self-occlusion of the target. Using this metric, we show the impact of occlusion on optimal camera pose by analyzing several camera configurations. Finally, we show camera placement examples that demonstrate how this metric can be applied toward the automatic design of more accurate and robust tracking systems.