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Robust real-time periodic motion detection, analysis, and applications (2000)
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| Venue: | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE |
| Citations: | 301 - 14 self |
BibTeX
@ARTICLE{Cutler00robustreal-time,
author = {Ross Cutler and Larry S. Davis},
title = {Robust real-time periodic motion detection, analysis, and applications},
journal = {IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE},
year = {2000},
volume = {22},
number = {8},
pages = {781--796}
}
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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.
Keyphrases
robust real-time periodic motion detection periodic motion object classification lattice structure new technique motion segmention motion-based recognition time-frequency analysis nonstationary periodicity similarity matrix person counting real-time system person detection motion symmetry self-similarity measure index term periodic motion
