Object Recognition And Motion Tracking Based On Video

Computer Vision aims at enabling and advancing intelligent perception of inputimage data. Moving object recognition, detection and tracking, are an important ar-eas in computer vision research. The related algorithms not only can be used in videosurveillance and trafc regulation, but also as a basis part for other algorithms suchas robotics, authentication systems, and human-computer interaction. Meanwhile, thedetectionandtrackingofmovingobjectscanprovideafoundationforhigher-levelcom-putervisionanalysis. Therefore,thedetectionandtrackinghasbeenanimportantfocusof computer vision research. In the last decade, people designed many ways to solvethis issue.However, in view of the complexity of the identifcation problem, there are still alot of difculties on the accuracy of identifcation and detection tracking and the real-time requirement. No gerneral method can be applied to all scene. For example, thecalculation of traditional learning-based classifer depends on the feature extractioncapacity. It is unable to meet the real-time requirements. And some tracking methodsdetectthemovementoftheimageblockfrominterframediferential, butthosemethodsignore the advantage of reducing the time complexity by narrowing down the range oftrack recognition.This paper fstly summarizes and analyzes the relevant theories, technologys andalgorithms of object recognition and tracking, and especially give a detailed descrip-tion of performance analysis for a method based on statistical learning identifcationmethod and the motion prediction. Secondly, based on the achievement of researchat home and abroad, this paper puts forward a method based on feature learning andmotion prediction object recognition and tracking algorithm, and shows its application that based on HAAR and HOG features for vehicle recognition and tracking system.The system can not be restricted by the variable background scene, and be improvedaccording the diferent features’ classifers.Useing motion prediction, the method canimprove vehicle recognition accuracy and guarantee real-time, can be applied on ve-hicle detection and tracking in complex scene. Finally, the paper describes the systemframwork of the vehicle detection system based on the above algorithm, analyzes theexperiment results, and compare the pros and cons of with the traditional algorithms.Experiments can verify the excellent performance of the algorithms in real-time andaccuracy. Meanwhile, this paper also analyzes the theoretical reason of the case ofmistaken recognition, and gives some suggestions and prospects for future research.In summary, the algorithm in this paper based on the real-time vehicle locationprediction model, can achieve real-time detection and tracking for video stream pro-cessing with enough accuracy. With a wide range of application value, it is suitableas a vehicle driver assistance system particularly and the technology foundation forautomatic drive.