Research And Application Of The Video Surveillance Technology On Intelligent Transportation Systems

Because traffic video information has been collected and used as an important part of traffic surveillance and management, great importance has been attached to video surveillance technology which is based on video image processing, analyzing, and understanding, to improve intelligence of traffic surveillance and management. As a subject of general interest in area of image processing and computer vision, video surveillance technology is different from traditional surveillance technology in that it is highly intelligent; therefore, research in this technology and its application in Intelligent Transportation System are of great practical significance. Focusing on the key technological problems of traffic video surveillance, the main research work of this paper is presented as follows:1. The principles of four methods of moving objects detection are given. By comparing and analyzing different methods and their characteristics, the drawbacks of these methods are pointed out and ways of improvement are offered. The drawback of Background Subtract Method is that the background is very sensitive to noise. So three new methods of background renewal are given to reduce the disturbance of noise. To solve the problem of detecting objects in frequently changing background, a method of constructing background model is proposed. Meanwhile, an improved Inter-frame Difference method is given to tackle the problem that moving objects cannot be wholly detected with Inter-frame Difference method. To overcome the drawbacks of optical flow method, i.e., the calculation involved is huge while there is time delay when detecting, an improved optical flow method which integrates with inter-frame difference method, is given.2. To deal with the problem of detecting moving objects in nonstationary complex environments, research is conducted regarding the relationship between inter-frame color co-occurrences and frequent foreground and background change. Based on the observation that inter-frame color co-occurrences are much significant for frequent changes in background than in foreground, a Bayes decision rule for classification of background and foreground changes based on inter-frame color co-occurrence statistics is derived. Methods to update background model in nonstationary environment are studied, and a way to maintain background model by updating reference background image and updating color co-occurrences is presented. Short-term and long-term strategies of updating the frequently changing background model are proposed. Based on the studies above, a novel method is given, which is based on color co-occurrence and Bayes decision rule, to detect foreground objects.3. An improved method of region based multiple target tracking is proposed. First, matching function is established according to the motion continuity principle of frame sequence in motion images so that matching test based on the characteristics of motion target zone can be conducted in the matching process. Second, Kalman filtering method is used to predict the next position of the target in the target zone so that the matching scope is narrowed and the searching speed is accelerated. Next, tracking control list is set up to record the newest motion parameter in the target zone to ensure the continuity of motion target tracking. Then, the missing problem oftarget caused by repeated record and pause is solved by exploiting centroid distance and delay searching. Finally, entering zone and leaving zone are set to judge the appearance of new target and disappearance of old target. Through all the above, correct tracking of targets is realized.4. A method based on Support Vector Machine to classify traffic objects is proposed. In order to classify the three kinds of traffic objects, that is, people, small vehicles, and large vehicles, there are three SVM classifiers to be built respectively. After training these classifiers with those samples of three kinds of traffic objects respectively, these classifiers can be used to classify the traffic objects. The results of experiments show that people, small vehicles and large vehicles can be classified correctly with the method proposed.5. Theories of video database management are studies. Traffic video information management system is designed, incorporating the relevant theories of video database management with the characteristics of traffic video information and the need for management of the information. The system can structuralize unstructured traffic video data, that is, to segment traffic video information into of shots, according to the video content, and then extract one or several key frames from each segment to represent this video segment. When the video data is stored, key frames and video segment will be stored separately. Index of key frames can be stored according to their content, so that the storage and search of the traffic video information can be conducted according to the content of the information mainly by identifying the characteristics of the target, e.g. color, texture and shape, etc.6. Based on the researches above, the system of "Regional Traffic Control Visualized Data Processing Platform" is designed and realized . Three application systems, which are based on this platform, are introduced. Research in the relevant technology is also conducted. In the plate recognition system, a method to locate plate by video detecting and color information is proposed. With this method, the plate regional image is captured directly with video detection technology and then the plate image is located and retrieved accurately with plate color information. In traffic violation detecting system, a method to distinguish violation is presented; in traffic video detecting system, methods to improve detecting accuracy and measures to accelerate the speed of detecting are also worked out.The main research work of this paper is supported by the Intelligence transportation Project of the Ministry of Public Security, "Research on Regional Traffic Control Visualized Data Processing Platform" (Intelligent Transportation theme: 20036152201) and the Project of Tackling Key Problems in Science and Technology of Xi'an, "Regional Transportation Video Intelligence Control Management System" (Intelligent Transportation video technology sub-item: GG04020).