Abstract
Due to the application requirement of single source tracking in dynamic background, it is difficult to get accurate pedestrian target detection. Through comparing performance quality of pedestrian describing operator between CENTRIST and HOG, we adopt CENTRIST feature extraction to combine with SVM off-line classifier and to train off-line model for target detection. Furthermore, we propose the importance to utilize edge classification thought to deepen contour feature. Meanwhile, we put forward the scheme to remove local texture and background noise to improve algorithm detection performance. The experiment offers detection performance comparison based on INRIA pedestrian detection data set in practical teachers' recording scene. It also explains that the improvement and optimization for CENTRIST pedestrian detection algorithm are very effective.
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