Vehicle Video Detection And Tracking Algorithm

With the development of computer hardware and computer vision technology,a computer vision-based traffic monitoring system has become possible. Vehicle detection and tracking real-time system based on video is the key to traffic monitoring system. Many popular related technology didn't meet vary requirements. Therefore,an efficient and robust detection and tracking system is needed eagerly.The aim to this thesis is to design video-based vehicle detection and tracking system,which can limit the noise of system and pedestrian factor,and used in large area,multiple objects and complex environment in traffic surveillance.The thesis has implemented adaptive background model to detect moving objects. We propose to operate in the Hue-Saturation-Value (HSV) color space,instead of the traditional RGB space,and show that it provides a better use of the color information,and naturally incorporates gray-level only processing. At each instant,the system constructs three Gauss distribution for a pixel and maintains an updated background model,and a list of occluding regions that can then be tracked. However,problems arise due to shadows. In particular,moving shadows can affect the correct localization,measurements and detection of moving objects. This work aims to present a technique for shadow detection and suppression used in adaptive color background model mentioned as before. The major novelty of the shadow detection technique is the analysis carried out in the HSV color space to improve the accuracy in detecting shadows.Moreover,based on these objects segmented,the paper discusses the tracking model based on recursive extended Kalman filter (EKF). Motion estimation based on EKF accurately estimates the feature's position so as to decreases the size of the region matching feature. In feature extractor,the paper combines centriod of the moving objects to tracking window to distinguish pedestrian and moving vehicle. Similar functions are applied for matching feature. Finally,the system updates adaptive tracking model by means of all kinds of situation,they are new moving objects,moving objects disappearing and pause. We partitions the image to three plots,intake area,tracking region,exit region.Results from experiments show that the model of HSV adaptive background with shadow detection and extended Kalman filter tracking has segmented moving objects and detected shadow so easy and accurately tracked moving vehicles in large area,multiple objects and complex environments. And the system has flexiblemathematic model and can meet real-time and practicality requirements.