Adaptive Silouette Extraction and Human Tracking in Complex and Dynamic Environments

Extracting a human silhouette from an image is the enabling step for many high-level vision processing tasks, such as human tracking and activity analysis. Although there are a number of silhouette extraction and human tracking algorithms proposed in the literature, most approaches work efficiently only in constrained environments where the background is relatively simple and static. In this work, we propose to address the challenges in silhouette extraction and human tracking in a real-world unconstrained environment where the background is complex and dynamic. We extract features from image regions, accumulate the feature information over time, fuse the high-level knowledge with low-level features, and build a time-varying background model. We develop a fuzzy decision process to detach foreground moving objects from the human body. Our experimental results demonstrate that the algorithm is very efficient and robust.