Research On Pedestrian Detectioon And Tracking Algorith Based On Image Sequences

More and more people are paying attention to intelligent video surveillance. Intelligent video surveillance uses technologies such as digital image processing, artificial intelligence and computer vision to process and analyze videos automatically in real-time, which can warn or alarm suspicious targets, potential hazards or violations in monitored scenes.This dissertation researched pedestrian detection and tracking algorithm under different conditions, including illumination change, occlusion and fast movement.Main work of this dissertationis listed as follows:1) To solve the problem ofpedestrian detection and tracking under different conditions, including illumination change, occlusion and fast movement, this dissertation proposed a pedestrian detection algorithm fusing histogram of gradient direction and textural features aiming at the interested areas. During pedestrian detection process, detector adjusts the scanning scale of the pedestrian detector by introducing pedestrian tracking result information to reduce the level and scope of the scanning of the detector. The detector only detects in the region of interest (The area where there is a high probability of pedestrian) to improve the pedestrian detection rate.The features merged histogram of gradient merging and textural features. Experimental results show that the improved histograms of gradient merging with local binary pattern scan not only improve the recognition rate, but can alsoand solve the occlusion problem to a certain extent.2)To solve the "drift" problem in pedestrians tracking, this dissertation researched and analyzed pedestrian tracking algorithm based on classifier.We proposed a method to combine offline pedestrian trackingand online pedestrian tracking templates. The Offline pedestrian detection results can introduce priori knowledgeinto the tracker. And it did not depend on the learning process to update so that it can avoid "drift" caused by the error accumulation of learning. This algorithm not only solved the problem of online pedestrian tracking initialization, but also solved the problem of selection of training data. As offline pedestrian classification, when pedestrians were presented in the scenes again after being occluded or the tracker lost targets, this method can re-recognize the pedestrians and keep tracking, so that the long time tracking can be achieved.