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视频转换图片以及运动检测，hog和SVM行人检测-Video conversion and image motion detection, hog and SVM pedestrian detection
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matlab行人检测所需要的行人图片素材，128*64像素图片-matlab pedestrian detection require pedestrians picture material, 128* 64 pixel image
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行人检测的程序，用于检测行人，并将行人进行简单的分割出来，便于后续的处理。-Pedestrian detection procedure for detecting pedestrians and pedestrians simple segmented to facilitate subsequent processing.
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深度学习的行人检测ccf网络结构，接口matlab接口。-Learning depth pedestrian detection ccf network structure, the interface matlab interface.
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智能交通行人检测主要程序代码，主要是行人视频的检测-Intelligent traffic pedestrian detection of the main program code, mainly pedestrian video detection
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一篇论文，主要是行人检测方面的考核基准，基本上都在论文中有涉及-pedestrian detection
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基于matlab的行人检测代码
排名第2的目标检测算法：基于加州理工学院2009年行人检测的文章：Integral Channel Features（积分通道特征）(Pedestrian detection code based on MATLAB)
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根据2005年Dalal提出HOG特征计算原理，自己编写了HOG特征提取算法，此特征可以用于目标检测中，包括行人检测，采用编程语言为C++，采用视觉库为opencv(According to the principle of HOG feature calculation proposed by Dalal in 2005, HOG feature extraction algorithm has been compiled. This feature can be used in object
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利用积分图的方式提取图像hog特征，用于人脸、行人检测。(Using the integral plot to extract the image hog feature, for human face, pedestrian detection.)
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利用Hog特征和SVM分类器进行行人检测(Using Hog features and SVM classifiers for pedestrian detection)
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运用HOG和SVM方法进行行人检测，同时使用难例优化算法(Using HOG to extract features and SVM to train the model for pedestrian detection.)
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在opencv上实现hog特征的提取，相关的配置不多描述，参照opencv的配置(Pedestrian detection based on OpenCV)
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This paper presents a new robust method for pedestrian
tracking. This approach including three steps: pedestrian
detection, feature extraction and pedestrian tracking.
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行人检测的经典论文，基于faster rcnn写的(pedestrian detection)
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改程序设计是基于差分法的行人检测，能框出人行走的轨迹。(The design of the modified program is based on the difference method of pedestrian detection, which can frame the track of human walking.)
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根据生物特征识别来实现行人检测的系统，包含MFC窗口(26/5000
According to biometric identification to realize the pedestrian detection system, including MFC window.)
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基于python语言实现opencv自带svm检测行人(Opencv takes SVM to detect pedestrians)
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用MATLAB实现基于hog的行人检测中的求hog特征(Hog features for pedestrian detection)
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用Adaboost实现行人检测中的漏检率，与boosting进行比对。(Using Adaboost to achieve missed detection rate in pedestrian detection, compared with boosting.)
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行人检测的很好，现在很多数据库都用DPM做这个检测(Pedestrian detection is very good. Now many databases use DPM to do this detection.)
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