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Robust Real-Time Periodic Motion Detection, Analysis, and Applications
August 2000 (vol. 22 no. 8)
pp. 781-796
Abstract—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.
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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 and Machine Intelligence, vol. 22, no. 8, pp. 781-796, Aug. 2000, doi:10.1109/34.868681
