A Vision-based Intelligent Algorithm Research For Traffic Intersection Vehicle Detection

With the improvement of traffic management and traffic monitoring intelligent level, video surveillance technologies based on video image processing, analysis and understanding have drawn increasing attention recently. Among them, traffic detection and informant collection in Intelligent Transportation System(ITS) area has become an important issue in the computer vision technology, and the most basic part is vehicles detection, classification, tracking and conflict detection automatically. In this paper, new methods are presented based on exploration and research on the issues mentioned above and experiments are conducted to demonstrate the effectiveness of the proposed methods.The major research contents and academic contributions of this dissertation include:First, the issue of moving object detection is studied. The algorithm for shadow detection based on multi-feature information fusion in the framework of Markov Random Field(MRF) is proposed based on the analysis of traffic scene shadow features. Background subtraction based on Gaussian mixture model is employed for foreground moving object extraction and the improved morphological filtering algorithm is used for eliminating noise. Shadow detection is accomplished based on the feature information, such as color, edge, texture, spatiotemporal coherence between the foreground pixels and the corresponding background image pixels. A variety of characteristic information is integrated into MRF energy function and graph cut algorithm is used for minimizing MRF energy function. Finally, foreground object detection results are achieved with effective shadow elimination.Next, the problem of moving object classification is solved. As the object characterization and classification are the key factors affecting classification accuracy, a histogram of oriented gradient operator based on kernel principal component analysis is proposed, and a binary decision tree based support vector machines for multi-class classification is constructed. Among them, in the object characterization process, the moving objects histogram of oriented gradient is obtained, and Mean-Shift clustering algorithm is used to cluster them into a number of feature vectors with a high degree of similarity among the internal subsets. Then, the subsets are mapped to a high dimensional feature space, and linear principal component analysis is employed to achieve an effective description of the object feature. With the discussion above, accurate object classification results could be obtained by the constructed support vector machine classifier based on binary decision tree.To achieve an accurate tracking of moving objects with non-linear, non-Gaussian, multi-modal motion characteristics, a particle filter based on artificial immune algorithm is proposed. As particle degradation phenomenon occurs during the calculation by traditional particle filter algorithm, artificial immune algorithm is introduced into the particle filter's re-sampling process and colonel selection of the particles to maintain the particle sample collection diversity, which can alleviate the particle degradation phenomenon effectively. Aiming at the real-time and reliability problem of moving object tracking under complex background environment, we design an adaptive fusion object tracking algorithm based on the color feature and edge gradient feature. In the process of object tracking, the moving objects' color feature and edge feature are fused into the observation probability distribution of the particle filter to compensate the changes of object and background, and improve tracking robustness.The accurate traffic conflict detection results are the precondition of intersection safety assessment based on traffic conflict technique. Upon completion of trajectory clustering and conflict prediction, a traffic conflict discrimination method based on critical security zone is proposed. First, Mean-Shift method is used to cluster moving objects'initial trajectory set, and a number of trajectory subsets which could represent scene behavior pattern are obtained. Then, each trajectory category is constructed by (Hidden Markov Models)HMM, and all models'parameters are obtained using training samples. In the conflict detection process, we use partial trajectories to predict conflict occurrence, and then the appearance and severity of a conflict are determined based on the features as following datum, distance, speed and running orientation.