Research On Vehicle Detection Algorithm Based On Video Image

Vehicle detection in video images is a hot research topic in computer vision and intelligent transportation. Its application has appeared in many fields of visual intelligent traffic, such as traffic safety monitoring video and traffic statistics etc. Through the efforts of numerous scholars, vehicle detection technology of video image has achieved fruitful results, and detection results have obtained great improvement than before. However, Because the complexity of the real world, especially the detection environment is more changeable and complex, the current vehicle detection also exist many problems further to analyze and solve in video images. Based on this, firstly, this paper studies the detection algorithm of existing on vehicle technology based on video image, and on this basis, by combining with the problems appeared in the detection scene, makes new attempt to technology of vehicle detection from improving the real-time, accuracy and robustness of detection. To sum up, this paper has done the following work:1. This paper discusses deeply the vehicle targets detection theory in video image, and the classic detection algorithms have been proposed in the present stage, from the two direction of the feature classification and background subtraction detection. And taking the actual detection algorithm to expound the study and application of these algorithms in vehicle detection. At last, to the various problems existing in application scenarios in vehicle detection, this paper makes experimental test and comparison for these algorithms, analyze the respective advantages and disadvantages of existed detection algorithms, and the causes of test results of the corresponding algorithm, through actual vehicle detection experiments.2. To the detection model of vehicle detection using the characteristics classification, this paper analyzes framework theory of the vehicle detection using feature extraction and machine learning at first, describes and analyzes the theory of methods of feature extraction and machine learning respectively. In view of the problem of describe information insufficient using the current single feature in vehicle detection base on the feature classification, this paper adopts the combined characteristics of gradient features and geometry features in feature module, that is HOG-Haar features to describe the vehicle, improves on the description ability of the vehicle characteristics, and uses of integral image to speed up speed of the feature extraction, employs the PCA method to reduce the dimension of feature vector, speed up the efficiency of learning and testing to classifier, discharge the interference of redundant information to the accuracy of vehicle detection. Finally, this paper illustrates vehicle detection method using multi-feature cascade classification combined with cascade classification method, and does the practical experiments using the detection method to the vehicle, makes the analysis and comparison with other algorithms.Detection experiments prove the correctness of the detection method in this paper. 3. To the video vehicle detection using the background subtraction detection model, this paper analyzes the theoretical framework of background subtraction at first, then analyzes those representative background subtraction detection algorithm currently, and uses of these algorithms to do the vehicle detection experiments combined with the actual scene video, analyzes the corresponding experimental results. To those problems and challenges of detection algorithm in vehicle detection, This article employs a background subtraction method that ViBe combined with improved LBP feature to vehicle detection, background subtraction based on LBP texture and ViBe background subtraction, dose the test experiment of vehicle detection using this algorithm combined with the actual scene detection, and makes comparison and analysis with other algorithms, The result of experimental test and comparison analysis further verified the reliability of the background subtraction method this paper described in the vehicle detection.