关键词: 特征提取, 目标分类, 支持向量机, Kalman滤波, 人眼视觉特性

Abstract: For solving the problem of human and vehicles classification in complex scene, a novel method based on multi-granularity perception SVM (support vector machine) is proposed. Firstly, motion regions of the video are detected and analyzed, and visual perception information is extracted by corner detection in motion regions. In order to reduce the noise interference, perception information is inferenced by Kalman filter in time-space domain. Furthermore, multi-granularity perception features of objects are constructed with the mass center of motion regions. Finally, a two-level SVM classifier is constructed, and classification results are obtained by training and classifying on the SVM classifier with multi-granularity perception features vector set. The results of experimentation show that the proposed method is good. The average correct rate of all-day classification between human and vehicles is up to 93.6% separately, and it is valid to avoid the influence of illumination, colors and object’s size variation. It is suitable for intelligent traffic system.

Key words: feature extraction, object classification, support vector machine, Kalman filter, human visual characteristic