Using interaction signatures to find and label chairs and floors

Our research takes an action-centered approach to automatically learning and classifying functional objects. Our premise is that interpreting human motion is much easier than recognizing arbitrary objects because the human body has constraints on its motion. Moreover, humans tend to interact differently with different objects, so you should be able to identify an object by analyzing how people move when they manipulate it. We call these motions the human-object interaction signature. An interaction signature is a method to find and classify objects on the basis of how humans interact with those objects. The method addresses many key problems encountered in smart-home monitoring systems.     

A Study on Smoothing for Particle-Filtered 3D Human Body Tracking

Stochastic models have become the dominant means of approaching the problem of articulated 3D human body tracking, where approximate inference is employed to tractably estimate the high-dimensional (～30D) posture space. Of these approximate inference techniques, particle filtering is the most commonly used approach. However filtering only takes into account past observations—almost no body tracking research employs smoothing to improve the filtered inference estimate, despite the fact that smoothing considers both past and future evidence and so should be more accurate. In an effort to objectively determine the worth of existing smoothing algorithms when applied to human body tracking, this paper investigates three approximate smoothed-inference techniques: particle-filtered backwards smoothing, variational approximation and Gibbs sampling. Results are quantitatively evaluated on both the HumanEva dataset as well as a scene containing occluding clutter. Surprisingly, it is found that existing smoothing techniques are unable to provide much improvement on the filtered estimate, and possible reasons as to why are explored and discussed.