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Robust real-time periodic motion detection, analysis, and applications

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2 Author(s)
R. Cutler ; Dept. of Comput. Sci., Maryland Univ., College Park, MD, USA ; L. S. Davis

We describe new techniques to detect and analyze periodic motion as seen from both a static and a 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 2D 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 nonstationary periodicity are provided

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:22 ,  Issue: 8 )