@conference {13192,
	title = {Pedestrian classification from moving platforms using cyclic motion pattern},
	booktitle = {Image Processing, 2005. ICIP 2005. IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/09//},
	pages = {II - 854-7 - II - 854-7},
	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.},
	keywords = {analysis;, angle;, body, classification;, compact, cyclic, DETECTION, detection;, digital, Feedback, Gait, human, image, information;, locked, loop, loop;, loops;, module;, MOTION, object, oscillations;, pattern;, pedestrian, phase, Pixel, principle, representation;, sequence;, sequences;, SHAPE, system;},
	doi = {10.1109/ICIP.2005.1530190},
	author = {Yang Ran and Qinfen Zheng and Weiss, I. and Davis, Larry S. and Abd-Almageed, Wael and Liang Zhao}
}
