视觉辅助导航是智能车辆导航领域的研究热点之一，其中道路与行人检测技术是其视觉导航系统的重要组成部分。
Vision navigation is the key technique of intelligent vehicle, and the lane and pedestrian detection is the most important technique.
行人检测是智能车辆辅助导航系统中的一项关键技术，也是目前计算机应用领域的研究热点之一。
The technique of moving pedestrian detection is one of the key techniques for Intelligent Vehicle Navigation System and is coming to a hot spot of research in computer application field at present.
系统包含两个主要功能模块：行人检测与识别、行人运动跟踪。
The designed system consists of two function modules: the pedestrian detection module and the pedestrian tracking module.
行人检测技术在智能控制系统、虚拟现实、机器人应用等方面也将得到广泛地应用。
Method of moving pedestrian detection will also be widely used in the intelligent control system, virtual reality, robot applications.
为了利用立体视觉技术提升视觉客流统计系统的准确率，提出一种基于区域视差提取的单、双目处理技术相结合的行人头顶部检测新方法。
A method based on contour matching to extract the head disparity was presented, in order to improve the accuracy rate of vision-based passenger flow estimation system by using the depth information.
该系统能够实时地检测出复杂背景中行人的数目，并且能够把包含行人的图像记录下来，具有视频回放功能。
This system can detect passengers from the complicated background and save the image of passenger for replaying in real-time.
本文研究静止摄像机场景视频中行人检测和行人跟踪计数方法，为设计实现一种实时有效的行人计数软件系统提供基础。
In this thesis, we study the pedestrian counting methods in the static camera scenes, and design a real-time pedestrian counting software system.
为了利用立体视觉技术提升视觉客流统计系统的准确率，提出一种基于区域视差提取的单、双目处理技术相结合的行人头顶部检测新方法。
To improve the accuracy rate of vision-based passenger flow estimation system by using stereo, a novel method based on regional disparity extraction to detect passengers head was presented.
为了利用立体视觉技术提升视觉客流统计系统的准确率，提出一种基于区域视差提取的单、双目处理技术相结合的行人头顶部检测新方法。
To improve the accuracy rate of passenger flow estimation during rush hour, a vision-based procedure to estimate passenger flow in buses was presented for the embedded application.
本文讨论了利用肤色信息进行人脸检测后，采用伪二维隐马尔可夫模型通过训练和识别两个基本部分建立起人脸识别系统。
In this paper we set up a face recognition system based on P2D-HMM with the two steps of drilling and recognizing after the face detection of complexion information.
本文提出并实现了一套完整的行人跟踪系统，整个系统的底层是由HOG特征和颜色直方图特征构成的行人检测器，上层则采用粒子滤波器算法，结合各个行人检测器的结果得到最终检测结果。
In this article, we proposed and implemented a human tracking system whose bottom layer is human detectors based HOG feature and color histogram feature, and upper layer is based on particle filter.
本文提出并实现了一套完整的行人跟踪系统，整个系统的底层是由HOG特征和颜色直方图特征构成的行人检测器，上层则采用粒子滤波器算法，结合各个行人检测器的结果得到最终检测结果。
In this article, we proposed and implemented a human tracking system whose bottom layer is human detectors based HOG feature and color histogram feature, and upper layer is based on particle filter.
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