An improved pedestrians detection algorithm using HOG and ViBe

The Pedestrian detection using Histograms of Oriented Gradients (HOG) is the most popular method to detect a human from a picture. However, it, calculates the HOG description, will cost too much time and can´t meet the real-time request for detecting pedestrian from the video surveillance system. In this paper we present a novel algorithm for detecting a human from a video. Firstly, The improved approach of Vibe follows a new background model using the temporal information, and present a new post-processing method for expanding the outlines of the foreground objects and then extract the foreground objects zone. Secondly, calculating the HOG feature of the extracted zone, and then send into the SVM classifier which has been trained to judge where is pedestrian or not. The combination algorithm, the improved Vibe and the HOG pedestrian detection, can save the processing time and the simulation results show that the proposed algorithm, compared with the traditional pedestrian detection algorithm, can detect pedestrian more accuracy and efficiency and its optimization ability is stronger.