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We present a method that is capable of tracking and estimating pose of articulated objects in real-time. This is achieved by using a bottom-up approach to detect instances of the object in each frame, these detections are then linked together using a high-level a priori motion model. Unlike other approaches that rely on appearance, our method is entirely dependent on motion; initial low-level part...
This paper presents a method that is capable of robustly estimating gait phase of a human walking from a sequence of images using only low-level motion. The approach we adopt is first to learn statistical motion models of the trajectories we would expect to observe for each of the main limbs. We then extract a sparse cloud of motion features from an image sequence using a standard feature tracker...
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