The Expansion Of EMCV Library And Implementation Of Embedded Vehicle Detection System

With the progress of technology, vehicle not only brings convenience to human, but also brings social problems. Traffic security become a widespread concern topic, meanwhile the demand of transportation related technologies is increasingly improved. Vehicle detection is the premise and foundation of the intelligent transportation research, such as vehicle tracking, vehicle models classification, vehicle speed test and so on, which is also a vital step of the forward collision warming technology and active safety technology. Vehicle detection is detecting the vehicle in complicated conditions.In the thesis, the general moving object extracted algorithms are compared, and Adboost algorithm that belong to pattern recognition is applied to the vehicle detection system, which can achieve vehicle detection without objective extracting, background modeling or updating. Adboost algorithm is divided into Discrete Adboost algorithm, Real Adboost, LogitBoost and GentleBoost, each algorithm has its own advantages and disadvantages. Finally, GentleBoost algorithm is selected to implement the vehicle system, and its accuracy and feasibility are approved in the thesis.In the thesis, EMCV library is expanded, and the vehicle system is achieved based on GentleBoost algorithm. The vehicle detection system is based on DSP system, according to the hardware module. And the software module is designed, which include memory allocation, DSP/BIOS system framework building, software driver and communications between the system threads. Considered the actual application, the vehicle detection classifier is established, after a great deal of experiments and tests, the author identify the principles and consideration of positive and negative samples selecting and the classifier training processing as well as how to set the classifier parameters, etc, and test Adboost algorithm performance on OpenCV platform.,the author implement the vehicle detection system by running the EMCV library on DSP, loading the former classifier, setting the parameters of input/output video port, creating the task of video capturing, displaying and video image processing, capturing the video stream and marking the vehicle information.