Wael AbdAlmageed

Pedestrian classification from moving platforms using cyclic motion pattern

TitlePedestrian classification from moving platforms using cyclic motion pattern
Publication TypeConference Paper
Year of Publication2005
AuthorsY. Ran, Q. Zheng, I. Weiss, L. S. Davis, W. Abd-almageed, and L. Zhao
Conference NameIEEE International Conference on Image Processing (ICIP)
Abstract

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 of a fast online pedestrian classification system which operates on videos acquired from moving platforms.

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