| Intelligent vehicle as the key technology and important part of Intelligent Traffic System (ITS) has been regarded as the effective way to resolve these traffic problems. The research of intelligent vehicle involve in image processing, pattern recognition, artificial intelligence, automatic control, sensing technology and so on. It has integrated the latest achievements of the information science and artificial intelligence. Thus, the research of intelligent vehicle has important values of practical and academic theory.Intelligent vehicle is an intelligent agent which integrates the functions of environmental perception, planning decision and operation control. In the lots of research tasks of intelligent vehicle, the information perception of driving environment is the foundation and key of all works. Machine vision is the key technology of information perception, and its success or failure will directly decide the survive space of intelligent vehicle in the future. Furthermore, the object detection and tracking is the most important and indispensable function in information perception, and is also the precondition of preventing risk and safe driving for intelligent vehicle. At present, the object detection technical in highway traffic scenes has been maturely almost, but it can not meet the practical requirement in complex urban traffic scenes. Thus, it is urgent and hot problem to develope the research of object detection for intelligent vehicle in complex urban traffic scenes. This paper further discusses the vision-based object detection, recognition and tracking in complex urban traffic scenes for intelligent vehicle. Main study content has been summarized as follows:Firstly, the research progress and typical systems of intelligent vehicle in domestic and international are reviewed, the main research contents and the key techniques of intelligent vehicle are also discussed. Based on the comparison of common information perception methods, the main technical schools of object detection in intelligent vehicle are discussed, and these research progresses are reviewed. Furthermore, the shortages of current research and the direction of future research are proposed in the paper.Secondly, the machine vision theory and three representative vision theory models are discussed in the paper. A new application-based improved computing vision theory model is established and provides the guiding for the practical algorithm design. From the three views of candidate object detection, object recognition and object tracking, the research progress of vision-based object detection in intelligent vehicle is reviewed. A practical monocular vision-based object detection algorithm model is presented, in which the feasibility of monocular vision and the general algorithm architecture of the paper are discussed.Thirdly, the monocular vision-based candidate object detection method is discussed detailedly in the paper. The common visual characters for candidate object detection in intelligent vehicle are discussed, and the theory principle of the singular signal detection based on Wavelet Transform Modulus Maxima (WTMM) is introduced. Furthermore, a novel WTMM-based candidate object detection method is presented in the paper.Experiment shows that the method can directly detect the candidate objects from the whole image plane without road constraint, overcome the problem of the object character submerged by the background and other objects, and meet the robustness acquirements of intelligent vehicle in complex urban traffic scenes. Fourthly, the vision-based object recognition method is discussed detailedly in the paper. The statistical learning theory and the principle of Support Vector Machine (SVM) are introduced, the main SVM-based multi-class classification methods are analysed, and the ensemble learning theory is discussed. Furthermore, a multi-class classification method based on binary tree SVM (BT-SVM) improved by mixtures of kernels and ensemble learning is presented in the paper. In the proposed method, a decision-tree structure is designed based on the distributing probability and pattern diversity of common objects in urban traffic scenes. The classifier function is designed based on the mixtures of kernels. Moreover, the parameters of classifier function are adaptively selected based on AdaBoost. The proposed method effectively improves classification accuracy and generalization ability of the classifer. Experiment shows that the method can effectively resolve the multi-class classification problem including vehicle, pedestrian, non-motor vehicle, background, etc in urban traffic scenes, especially the small samples condition.Fifthly, the vision-based object tracking method is discussed detailedly in the paper. The theory of no-parametric kernel density estimation based on Mean Shift algorithm is introduced, and the algorithm convergence condition is discussed briefly. Furthermore, a Mean Shift-based intelligent vehicle object tracking method is presented in the paper. In the proposed method, the object color space is used as feature space, the color feature statistical histogram is used to describe the target model, the Bhattacharyya coefficient between target model and target candidate model of adjacent frames is defined as the similarity metric, and the Mean Shift iterative algorithm is applied to locate the tracked object. The proposed method emphasizes to improve the following key problems of classical Mean Shift algorithm: 1) the Kalman-based motion prediction is introdced to improve the tracking initial point selection of maneuvering object; 2) a block matching strategy is presented to improve the processing for object occlusion in complex traffic scenes; 3) a selective submodel updating mechanism based on matching contribution degree is presented to overcome the model migration problem during tracking and guarantee the stability of long-time tracking in the practical application.Finally, the conclusion of whole research work in the paper is given. Furthermore, the further work and research prospects are introduced.
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