Research Of Model-Based Traffic Flux Information Detection System

Computer Vision is used to understanding the content of images, which are widely used in Intelligent Traffic System (ITS) . The video based traffic information detecting technology has detecting more information, larger surveillance areas, lower maintenance cost with respect to other sensors. The video detecting method is superiority over other technologies in vehicle tracking, vehicle detecting, and queue detecting, etc. It is the most important technology for traffic information detection. Supported with the project of Research for 10th Five-Year Plan -Development of Traffic Detection Device, this paper has researched a novel traffic flux detection and traffic scene understanding theory and technology with still cameras, with the research of embedded system, and successfully finished integrative and discrete traffic information detecting devices.Vehicles segmenting, tracking and recognizing is the basis of traffic information detection, it is the key technology for understanding traffic sense. Thinking about the detection device functional and real-time requirement, with knowledge of vehicle shapes and running style, this paper propose a new cubical model-based vehicle description and recognition system, which content includes bottom-layer features detection, object modelize, and object recognition. We develop the efficient algorithms for camera calibrating, motion detecting, model-based object recognizing, etc. We have successfully designed two kind embedded traffic information detection devices.In the research of camera calibration algorithm, with knowledge that the vehicle is running on the road-plane, the algorithm is developed into two stages - extrinsic parameter and intrinsic parameter estimating, that can reduce the .difficulty of camera calibrating. This paper researches the relation between road-plane restriction and road reprojected velocity, inferring the vehicles have the same direction to road reprojected velocity, which is used to initialize vehicle's locations in recognize stage. This paper researches processing algorithms of single frame image, such as wavelet transform, pyramid decomposition, multi-resolution, edge description, corner detection, and parallelogram based edges perception organization.Motion information detection algorithm is separated into change detecting and optical flow detecting for two stages. Change detection is used background different and temporal different operator. The temporal difference result is used "AND" operator to remove "GHOST', used background difference to complement the complete moving region. This paper research the background model, background updating, AGC compensation, morphologic processing and block convex segment, etc. The optical flow detection is used block matching for coarse detection and Lucas-Kanade algorithm for fine detection, used RANSAC for 2D affine robust motion model estimating. Furthermore, it describes the color-space transformed shadow detection methods.Vehicle model is described the topological relationship of cube by vertexes, lines, planes, with the aspect view of 2D reprojected image. In the recognizing stage, model is located by motion information. For a single vehicle, it is recognized by the region's silhouette feature,inferring by Bayes networks. For mixltiple vehicles, they are segmented and recognize by edge features and optical flow information, with multiple geometry restriction to control the search depth and numbers of recognize trees.Finally, this paper describes the designing and development of traffic information detection device, with hardware system and software system.