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Detecting pedestrians is a challenging problem owing to the motion of the subjects, the camera and the background and to variations in pose, appearance, clothing, illumination and background clutter. The Region Covariance Matrix (RCM) descriptors show experimentally significantly out-performs existing feature sets for pedestrian detection. In this paper, we present an efficient features extraction scheme: the Integral CovReg, inspired from Region Covariance Matrix (RCM) descriptors, combined with SVM classifier for pedestrian detection.

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