3D Model-Based Tracking of Humans in Action: A Multi-View Approach

摘要

We present a vision system for the 3D model-based tracking of unconstrained humanmovement. Using image sequences acquired simultaneously from multiple views, we recover the 3D body pose at each time instant without the use of markers. The pose-recovery problem is formulated as a search problem and entails finding the pose parameters of a graphical human model whose synthesized appearance is most similar to the actual appearance of the real human in the multi-view images. The models used for this purpose are acquired from the images. We use a decomposition approach and a best-first technique to search through the high dimensional pose parameter space. A robust variant of chamfer matching is used as a fast similarity measure between synthesized and real edge images. We present initial tracking results from a large new Humans-In-Action (HIA) database containing more than 2500 frames in each of four orthogonal views. The four image streams are synchronized. They contain subjects involved in a variety of activities, of various degrees of complexity, ranging from simple one-person hand waving to two-person close interaction in the Argentine tango.