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Issue No.08 - August (2000 vol.22)
pp: 781-796
ABSTRACT
<p><b>Abstract</b>—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.</p>
INDEX TERMS
Periodic motion, motion segmention, object classification, person detection, motion symmetries, motion-based recognition.
CITATION
Ross Cutler, Larry S. Davis, "Robust Real-Time Periodic Motion Detection, Analysis, and Applications", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.22, no. 8, pp. 781-796, August 2000, doi:10.1109/34.868681
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