Abstract: The recognition of nonrigid motion, particularly that arising from human movement (and by extension from the locomotory activity of animals) has typically made use of high-level parametric models representing the various body parts (legs, arms, trunk, head etc.) and their connections to each other. Such model-based recognition has been successful in some cases; however, the methods are often difficult to apply to real-world scenes, and are severely limited in their generalizability. The first problem arises from the difficulty of acquiring and tracking the requisite model parts, usually specific joints such as knees, elbows or ankles. This generally requires some prior high-level understanding and segmentation of the scene, or initialization by a human operator. The second problem, with generalization, is due to the fact that the human model is not much good for dogs or birds, and for each new type of motion, a new model must be hand-crafted.

In this paper, we show that the recognition of human or animal locomotion, and, in fact, any repetitive activity can be done using low-level, non-parametric representations. Such an approach has the advantage that the same underlying representation is used for all examples, and no individual tailoring of models or prior scene understanding is required. We show in particular, that repetitive motion is such a strong cue, that the moving actor can be segmented, normalized spatially and temporally, and recognized by matching against a spatio-temporal template of motion features. We have implemented a real-time system that can recognize and classify repetitive motion activities in normal gray-scale image sequences. Results on a number of real-world sequences are described.