Abstract
Pedestrian detection is a hot topic in the field of computer vision in recent year. But the current studies about pedestrian detection mainly focus on feature extraction, training and classifier model and pay little attention to non-maximum suppression (NMS). This thesis uses the information like ratio of detection scores, neighborhood window to improve NMS based on HOG-SVM algorithm, solving the problems that alone windows in detected images arise false detection rate and the suppression windows surrounded by inhibited windows arise false detection rate and missing detection rate. Experiment results on the INRIA pedestrian database show that the improved non-maxima suppression can solve the above problems, reducing the false detection rate and missing detection rate in pedestrian detection.
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