Robust Real-Time Periodic Motion Detection, Analysis, and Applications

Abstract

We describe new techniques to detect and analyze periodic motion as seen from both a static and moving camera. By tracking objects of interest, we compute an object’s self-similarity as it evolves in time. For periodic motion, the self-similarity measure is also periodic, and we apply Time-Frequency analysis to detect and characterize the periodic motion. The periodicity is also analyzed robustly using the 2-D lattice structures inherent in similarity matrices. A real-time system has been implemented to track and classify objects using periodicity. Examples of object classification (people, running dogs, vehicles), person counting, and non-stationary periodicity are provided.

DOI: 10.1109/34.868681

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@article{Cutler2000RobustRP, title={Robust Real-Time Periodic Motion Detection, Analysis, and Applications}, author={Ross Cutler and Larry S. Davis}, journal={IEEE Trans. Pattern Anal. Mach. Intell.}, year={2000}, volume={22}, pages={781-796} }