Pedestrian detection using HOG-based block selection

Recently, pedestrian detection methods have been popularly used in the field of intelligent vehicles. In most previous works, the Histogram of Oriented Gradients (HOG) is used to extract features for pedestrian detection. However HOG is difficult to use in the real-time operating system of an intelligent vehicle. In this paper, we proposed a pedestrian detection method using a HOG-based block selection. First, we analyse the HOG block and select the parts of the block with a high hit rate. We then use only 20% of the total HOG blocks for the pedestrian feature. The proposed method is 5 times faster than methods using the entire feature, while performance remains almost the same.