Vehicle Detection And Tracking System Design In The Complex Road Scene

Video image processing is an important application of computer vision technology, is also an important branch of artificial intelligence, and we can eventually use the computer for processing the video image informationby the related art image processing algorithm. Video surveillance image processing technology arises in this context, and has been widely used. Thistechnology does not require human intervention, using the corresponding image processing algorithms that can automatically analyze surveillance video, it is set to complete certain functions, such as to target, target tracking. With the development of the city, now everywhere in the city you can see the installation of video surveillance, it bring security and stability to the benefits, but also potentially increasing workload management, therefore, accurate and fast and efficient video data processing is urgently needed to resolve.In short, Video image processing technology has developed rapidly in recent years, and gradually close contact with people’s production and daily life.The system is designed in this context, the issue introduces the background of the complicated road vehicle detection and tracking system, as well as the current research. Through analysis the image preprocessing, target detection, feature extraction, target recognition and tracking technologies, the issue identified several common basic algorithm of this topic used in system design and implementation.The system uses c++ programming language, input a video of the road, then output of the trajectory of the target vehicle’s image as well as the vehicle through the image preprocessing, target detection, vehicle identification and vehicle tracking,. The system consists of the following four modules:(1) Image preprocessing module, introduces the image processing and morphological processing to remove noise and run implemented in the system. Features binding experiments, this study uses a template-based image Gaussian smoothing.(2) Moving target detection module, introduces several common moving target detection algorithm, and compare those algorithms. An improved ViBe moving object detection method are propused based on this, the method combines image processing technologies, and ultimately meet the requirements of practical application in eliminating ghosting, shadowing, and regular exercise. Experiment shows that the system used the improved VIBE effect better, have a better effect about moving target detection, also can applicabled to the application system, and the resulting movement of the target image can be easily used as input data for the next step.(3) The vehicle identification module, mainly analyzed and discussed several commonly classification and characterization of common operators, and ultimately the support vector machines and HOG features are used in the vehicle classification. Experiments show that the vehicle identification algorithm design of the system can recognize most of the vehicles, and the recognition time complexity is low, also can meet the needs of real-time, so it is suitable for real-time identification of the vehicle. Meanwhile, vehicle identification algorithm of the present system also has shortcomings, such as low-resolution vehicle can not be detected, this deficiency will be addressed in the next work.(4) The moveing vehicle tracking module, introduces the common moving target tracking algorithm such as Kalman filtering and MeanShift algorithm, this paper implements the Meanshift tracking algorithm and the use of vehicle size and speed of the auxiliary track, through experiments and found that the Meanshirt tracking algorithm combining the vehicle speed and area is better and more efficient. The experimental results, the use of this algorithm can accurately track the vehicle and draw its trajectory.