Pedestrian Detection Method Based on YOLO Network
摘要
For improving the poor robustness of pedestrian detectors based on artificial extraction characteristics,learns from the advanced research achievements in the object detection field,proposes a method of real-time pedestrian detection.Being based on the YOLO network structure,combines with characteristics that pedestrians’ aspect ratio is small in images,selects appropriate numbers and scales of the initial candidate boxes by clustering,improves the YOLO network structure,adjusts density of candidate boxes distributed on the X and Y axis,forms a network structure which is suitable for pedestrian detection.Experiments in INRIA pedestrian datasets show that comparing with some pedestrian detection methods,such as HOG,LatSVM-v2 and so on,this method reduces the missed and false detection,improves the positioning accuracy and satisfies the real-time requirements.