关键词: 车辆交通图像, 光照特征, 支持向量机, K近邻算法, 光照检测

Abstract: In order to accurately detect the light type of vehicle traffic image, so as to correct different lighting to reduce its impact on the license plate positioning, a vehicle image lighting detection method based on improved K Nearest Neighbor and Support Vector Machine（KNN-SVM） is proposed. Firstly, the HSV spatial brightness feature, the gray histogram feature and the projection histogram feature are fused as the light feature of the vehicle image, and then the distance calculation method is improved in traditional KNN-SVM, which is redefined as the distance between each class of samples to the class of support vectors, and the testing and verification is performed on the collection of all-weather different light vehicle image. Experiments show that, the improved KNN-SVM advances the time to threshold acquisition, avoids repeated detection of traditional KNN-SVM for SVM detection and KNN detection in the vicinity of the hyperplane, it not only reduces the algorithm complexity and running time, but the detection accuracy is higher than the traditional KNN-SVM and the value of KNN or SVM alone, which is up to 99.67%.

Key words: vehicle traffic image, illumination feature, Support Vector Machine（SVM）, K Nearest Neighbor（KNN）, illumination detection