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Pedestrian classification from moving platforms using cyclic motion pattern

9 years 11 months ago
Pedestrian classification from moving platforms using cyclic motion pattern
This paper describes an efficient pedestrian detection system for videos acquired from moving platforms. Given a detected and tracked object as a sequence of images within a bounding box, we describe the periodic signature of its motion pattern using a twin-pendulum model. Then a Principle Gait Angle is extracted in every frame providing gait phase information. By estimating the periodicity from the phase data using a digital Phase Locked Loop (dPLL), we quantify the cyclic pattern of the object, which helps us to continuously classify it as a pedestrian. Past approaches have used shape detectors applied to a single image or classifiers based on human body pixel oscillations, but ours is the first to integrate a global cyclic motion model and periodicity analysis. Novel contributions of this paper include: i) development of a compact shape representation of cyclic motion as a signature for a pedestrian, ii) estimation of gait period via a feedback loop module, and iii) implementation ...
Yang Ran, Qinfen Zheng, Isaac Weiss, Larry S. Davi
Added 23 Oct 2009
Updated 14 Nov 2009
Type Conference
Year 2005
Where ICIP
Authors Yang Ran, Qinfen Zheng, Isaac Weiss, Larry S. Davis, Wael Abd-Almageed, Liang Zhao
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