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A shape-based voting algorithm for pedestrian detection and tracking

By Phil Assheton and Andrew Hunter


This paper presents the MOUGH (Mixture of Uniform and Gaussian Hough) Transform\ud for shape-based object detection and tracking. We show that the edgels of a rigid\ud object at a given orientation are approximately distributed according to a Gaussian\ud Mixture Model (GMMs). A variant of the Generalized Hough Transform is proposed,\ud voting using GMMs and optimized via Expectation-Maximization, that is capable of\ud searching images for a mildly-deformable shape, based on a training dataset of (possibly\ud noisy) images with only crude estimates of scale and centroid of the object in each\ud image. Further modifications are proposed to optimize the algorithm for tracking. The\ud method is able to locate and track objects reliably even against complex backgrounds\ud such as dense moving foliage, and with a moving camera. Experimental results indicate\ud that the algorithm is superior to previously-published variants of the Hough transform\ud and to Active Shape Models in tracking pedestrians from a side view

Topics: G400 Computer Science
Publisher: Elsevier / Pattern Recognition Society
Year: 2011
DOI identifier: 10.1016/j.patcog.2010.10.012
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