Independent increment processes for human motion recognition

This paper describes an algorithm for classifying human motion patterns (trajectories) observed in video sequences. We address this task in a hierarchical way: high-level activities are described as sequences of low-level motion patterns (dynamic models). These low-level dynamic models are simply independent increment processes, each describing a specific motion regime (e.g., ''moving left''). Classifying a trajectory thus consists in segmenting it into the sequence its low-level components; each sequence of low-level components corresponds to a high-level activity. To perform the segmentation, we introduce a penalized maximum-likelihood criterion which is able to select the number of segments via a novel MDL-type penalty. Experiments with synthetic and real data illustrate the effectiveness of the proposed approach.