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

被引：389

作者：

Cutler, R ^{[1
]}

Davis, LS ^{[1
]}

机构：

[1] Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 2000年 / 22卷 / 08期

关键词：

periodic motion; motion segmention; object classification; person detection; motion symmetries; motion-based recognition;

D O I：

10.1109/34.868681

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 objects 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.

引用

页码：781 / 796

页数：16

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