The Research Of Key Technology Of Vehicle Identification Based On Vedio

In order to improve the efficiency of traffic system, the Intelligent Transportation Systems (ITS) is used to manage traffic world-widely. To build a complete ITS, the first task is to build an accurate and efficient traffic information collection system that collects characteristic traffic parameters in real time. Vehicle detection and recognition technology is the core of the traffic information collection system. Vehicle detection and recognition based on video is a hot research topic now. Vehicle recognition based on video is vehicle shape analysis essentially. Shape analysis consists of four parts:target shape extracting, shape feature expression, shape feature subtraction, and shape classification etc. The main work of this thesis is around these four parts:The cast shadow is always detected as moving object. If not eliminated, the cast shadow will cause great trouble for tracking and recognition of moving object. To solve this problem, a shadow removal algorithm based on multiple feature differences between pixels and the reference background pixels is presented. The performance of the proposed method better than that of other shadow elimination methods in recent years is proved by experiments.In real time shape recognition based on video, the shape descriptor is desired to characterize the shape accurately, and can be extracted fast. Polar Radius Height Functions (PRHF) shape descriptor is proposed to solve this problem. The descriptor contains the relative position description of the shape contour sampling points, which is able to accurately capture the shape contour features. A number of comparative experiments are conducted on3standard databases for comparing shape retrieval performance of PRHF and many popular shape descriptors in recent years. The shape retrieval performance of PRHF achieves similar or even higher than that of shape descriptor s in recent years is proved by experiments.In real-time shape recognition based on video, feature selection play an important role that can find out the important features to reduce the algorithm’s time and space complexity and to improve data quality and data generalization ability. A new vector similarity metric for learning vector quantization (LVQ) is advised, and a new feature selection evaluation criterion based on low loss function of LVQ classification is proposed. Based on the evaluation criterion, a feature selection algorithm named LVQMFS that optimizes the hypothesis margin of LVQ classification through minimizing its loss function is presented. Some experiments that compared with feature selection algorithms in recent years are carried out on several UCI data sets. The performance of new algorithm achieves similar or even higher than that of feature selection algorithms in recent years is proved by experiments. The feature data range is ignored when Euclidean distance used as a vector similarity metric, which affects the classification accuracy of the traditional learning vector quantization algorithm (LVQ) and its variants. To solve the problem, a novel vector similarity metric is proposed, and a new algorithm named as GLVQ-Range based on this metric and GLVQ is put forward. The classification accuracy and computation speed of the algorithm are tested in comparison to traditional alternative LVQ algorithms, using8datasets of UCI machine learning repository. The algorithm usability in real production environment is verified through the video vehicle classification data set.It is difficult to verify classification accuracy of vehicle classification based on video. To solve the problem, the fuzzy matrix for controlling of data clustering process is proposed, and then ant colony clustering algorithm based on fuzzy matrix is proposed. The experimental results show that fuzzy matrix ant colony clustering algorithm is correct and efficient, and verifying classification accuracy of real-time vehicle classification based on video is feasible.