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

Author

Cutler, Ross ; Davis, Larry S.

Author_Institution

Dept. of Comput. Sci., Maryland Univ., College Park, MD, USA

Volume

22

Issue

8

fYear

2000

fDate

8/1/2000 12:00:00 AM

Firstpage

781

Lastpage

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

Keywords

image classification; image motion analysis; matrix algebra; object recognition; real-time systems; stability; time-frequency analysis; 2D lattice structures; nonstationary periodicity; object classification; periodic motion analysis; periodicity; person counting; real-time system; robust real-time periodic motion detection; self-similarity; similarity matrices; time-frequency analysis; Cameras; Dogs; Lattices; Motion analysis; Motion detection; Motion measurement; Real time systems; Robustness; Time frequency analysis; Vehicles;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.868681

Filename

868681