Research On Pedestrian Detection And Tracking In Video Surveillance

With the rapid improvement of hardware performance,computer vision is developing fast.Pedestrian detection and tracking technology is the hotspot of computer vision and also the most fundamental application of computer vision.It plays an important role in surveillance,traffic security and human-computer interaction,so it is of great research value and has a bright market prospects.Although lot of researchers have been dedicated to improve the algorithm of pedestrian detection and tracking for decades,due to the inherent characteristics of pedestrians(different color and dress,non-rigid),the performance of pedestrian detection and tracking is far from practical application,so it's still a hotspot in computer vision.This paper is mainly focus on improving the real-time performance by taking advantage of the information of pedestrian movements to predict their positions.In consideration of that pedestrian are non-rigid objects,the models of pedestrian change a lot.We propose a method to update models in CMT algorithm to adapt to the change of pedestrian appearance,making it more suitable for long-term tracking.Our main work and innovation are elaborated in this paper as follows:The state-of-art pedestrian detection methods are mainly restricted by weak real-time performance.To alleviate this problem,we propose a fast pedestrian detection method by taking advantages of the motion information of pedestrian in this paper.It regards pedestrian as moving objects,focusing on moving region to remove useless background information,to speed up the following detection.By well-designed predicting method,we can predict the new location of the pedestrian in a new frame.This procedure reduces the amount of computation greatly,so it's the key for real-time pedestrian detection.To enhance the reliability of our method,we build a temporary model for every pedestrian in the scenario,and verify the predicted location with the temporary model.Finally,we experiment on various scenarios to verify the improved performance of our method.The part-based model is more suitable to deal with the deformation and occlusion of targets.Pedestrian is non-rigid and easy to be covered by other object,so part-based model enhances the robustness of pedestrian tracking.But the keypoints based model method CMT algorithm is initialized by the information of the first frame,making it not suitable for long-term tracking.In order to alleviate problems ofCMT algorithm,we are the first to propose a method to update the keypoints based model,and modify CMT algorithm to adapt to the dynamic model.To enhance the reliability of the result,we introduce the hierarchical clustering algorithm,checking the consistency of the matched keypoints,to remove outliers.Finally,the experiment results on 50 video sequences show that our modified method(AMT algorithm)are superior to the original CMT algorithm.