【作者】 胡楠；

【导师】 沈小艳；

【作者基本信息】 大连海事大学 ， 信息与通信工程， 2011， 硕士

【摘要】 随着科技的进步,车辆给人们的生活带来便利的同时也带来了社会问题,交通安全成为人们普遍关心的话题,同时对于交通领域的相关技术的要求也在不断的提高。车辆检测技术是车辆跟踪、车型分类、车速检测等智能交通方面研究的前提和基础,也是前方碰撞警示技术以及主动安全技术等至关重要的一步。车辆检测技术是指从复杂的交通信息中将目标车辆检测出来,即目标提取的过程。本篇论文分析比较常用的目标提取算法,将模式识别中的Adboost算法运用到车辆检测系统中,完成了可以不用通过背景建模、更新来实现车辆检测的功能。Adboost算法分为Discrete Adboost、Real Adboost、LogitBoost以及GentleBoost四种,每一种算法都有各自的优点和不足,本文选择了GentleBoost算法,并且证明了该算法在车辆检测系统中的应用具有可行性和准确性。本文对EMCV库进行了扩充,实现了基于GentleBoost的车辆检测算法。本文实现的车辆检测系统是基于DSP系统平台的,通过结合系统的硬件部分设计了系统软件部分,包括存储器分配、DSP/BIOS系统框架构建、软件驱动以及系统线程间的通信等问题。针对实际应用情况,建立了车辆检测分类器,经过大量的实验和测试,确定了选取正负样本、分类器训练过程中的原则和注意事项,以及参数如何设置等问题；使EMCV库函数可以在DSP上运行,将前面设计的适合该系统的分类器加载到该系统中,完成车辆检测系统的视频口采集和显示设置,建立视频采集和视频图像处理显示任务,采集视频流将车辆信息在视频帧图像上标记出来,使车辆检测算法在DSP系统平台上实现。更多还原

【Abstract】 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.更多还原