A new method for representing and recognizing human bodymovements is presented. Assuming the availability of Cartesian tracking data, we develop techniques for representation of movements basedon spacecurves in subspaces of a "phase space." The phase space has axes of joint angles and torso location and attitude, and the axes of the subspaces are subsets of the axes of the phase space. Using this representation we develop a system for learning new movements from ground truth data by searching for constraints which are in effect during the movement to be learned, and not in effect during other movements. We then use the learned representation for recognizing movements in data. Prior approaches by other researchers used a small number of classification categories, which demanded less attention to representation. We train and test the system on nine fundamental movements from classical ballet performed by two dancers. The system learns and accurately recognizes the nine movements in an un...

Temporal textures are textures with motion. Examples include wavy water, rising steam and fire. We model image sequences of temporal textures using the spatio-temporal autoregressive model (STAR). This model expresses each pixel as a linear combination of surrounding pixels lagged both in space and in time. The model provides a base for both recognition and synthesis. We show how the least squares method can accurately estimate model parameters for large, causal neighborhoods with more than 1000 parameters. Synthesis results show that the model can adequately capture the spatial and temporal characteristics of many temporal textures. A 95% recognition rate is achieved for a 135 element database with 15 texture classes. 1.