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HOG-Pedestrian-Detector

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投递者邹玮

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HOG-Pedestrian-Detector

This repository contains the code for a MATLAB implementation of a basic HOG + SVM pedestrian detector form my Computer Science Master thesis

Disclaimer

If you are going to use this code, please read the LICENCE and keep in mind that I PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND.

I partially adapted this code-base to newer versions of MATLAB but is very likely you find discrepancies in how some MATLAB functions work.I am in general happy to help understanding the project if you ask nicely but since the implementation of the project is now several years old and MATLAB has evolved, some functions might behave differently and I won't be updating the project continuously nor answering about how to update the code to newer MATLAB versions.

Requirements

MATLAB >= R2017b
libsvm >= 3.22

Installation

Please refer to MATLAB and libsvm documentation to install.

Data

Run

The project was developed on a Windows machine and now being resurrected on a Linux one, so you should be good in any platform as long as you can run MATLAB.

Setting the environment

Add the libs directory and all sub-directories to MATLABs path.Either through the command window or through the GUI.
Make sure libsvm is visible to MATLAB. If you are not sure if your installation of libsvm went alright, you can check with:

which -all svmtrain

Which should show something like:

~/HOG-Pedestrian-Detector/libs/libsvm-3.22/matlab/svmtrain.mexa64
/usr/local/MATLAB/R2017b/toolbox/stats/stats/svmtrain.m                 % Shadowed

There are several entry points to the project, but here the two main ones are shown:

Train

Assuming there's a models directory where trained models will be saved and that the positive and negative images can be found in dataset/Test/pos and dataset/Test/neg respectively.Train an SVM model named test

model = train_svm("test", ["./models", "dataset/Train/pos" "dataset/Train/neg"]);

Eval

To evaluate the just trained model:

test_svm(model.test, ["dataset/Test/pos" "dataset/Test/neg"]);

Note test_svm expects model.<model-given-name-to-train-function>...

PCA versions of train / test

[model, Ureduce] = train_svm_PCA("test_pca", ["./models", "dataset/Train/pos" "dataset/Train/neg"]);
test_svm_PCA(model.test_pca, Ureduce, ["dataset/Test/pos", "dataset/Test/neg"]);

Known issues & contributions

Old MATLAB version used to concatenate strings by enclosing them between squared brackets but doesn't look like valid any longer. In that case you should use the strcat function. For example when constructing paths, so:

path = ['folder', 'filename', '.extension']  % this is wrong!
path = strcat('folder', 'filename', '.extension')  % this is right!

If you enjoyed this repository and find things that are not working any longer, you are very welcome to open a PR with fixes and I'll happily introduce them.

License

使用案例

（收藏）行人检测(Pedestrian Detection)资源

一、论文 CVPR 2012 与行人检测相关的论文 [1] Contextual Boost for Pedestrian Detection YuanyuanDing, Jing Xiao [2] Understanding Collective CrowdBehaviors:Learning Mixture Model of Dynamic Pedestrian-Agents Bolei
hog lbp pedestrian

跑了An HOG-LBP Human Detector with Partial Occlusion Handling提供的代码，点击打开链接又是linux的，sh是linux的shell脚本,在windows下跑perl要安装activeperl。在原程序中加入perl语句sleep(time)，防止屏幕一闪而过。出现以下错误： error while loading shared libra
C4: Real-time pedestrian detection——C4实时行人检测算法

http://cs.nju.edu.cn/wujx/projects/C4/C4.htm Jianxin Wu实现的快速行人检测方法。 Real-Time Human Detection Using Contour Cues： http://c2inet.sce.ntu.edu.sg/Jianxin/paper/ICRA_final.pdf C4能够达到比现有人体检测算法更高的速度
图像处理学习笔记之：HOG+SVM特征详解与行人检测

1、HOG： HOG（Histogram of Oriented Gradient），即方向梯度直方图，是一种在计算机视觉和图像处理中用来进行物体检测的特征描述子。它通过计算和统计图像局部区域的梯度方向直方图来构成特征。其依据是所检测的局部物体外形能被光强梯度和边缘方向的分布所描述，本质就是梯度的统计信息，而梯度总是存在于图像的边缘（边缘是由图像中亮度变化明显的点组成的） 2、流程：
opencv+hog+svm行人检测自己训练

#include #include #include #include #include #include #include #include #include #include using namespace std; using namespace cv; //常量定义 char IMG_PATH_TEXT[]="E:/INRIAPerson/color/img_path.txt"; //
基于HOG描述子和支持向量机的python-cod行人检测

我试图理解python中使用HOG和SVM进行行人检测的代码，并用FPGA来加速。在下面的代码工作精细复制自一个网站hog = cv2.HOGDescriptor() hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector()) def detector(image): rects, weights = hog.detectMu
行人检测(Pedestrian Detection)资源

行人检测(Pedestrian Detection)资源原文链接 http://hi.baidu.com/susongzhi/item/085983081b006311eafe38e7 一、论文 CVPR2013年行人检测相关的文章 [1] Robust Multi-Resolution Pedestrian Detection in Traffic Scenes Junjie Y
利用Hog特征和SVM分类器进行行人检测

在2005年CVPR上，来自法国的研究人员Navneet Dalal 和Bill Triggs提出利用Hog进行特征提取，利用线性SVM作为分类器，从而实现行人检测。而这两位也通过大量的测试发现，Hog+SVM是速度和效果综合平衡性能较好的一种行人检测方法。后来，虽然很多研究人员也提出了很多改进的行人检测算法，但基本都以该算法为基础框架。因此，Hog+SVM也成为一个里程表式的算法

HOG-Pedestrian-Detector

HOG-Pedestrian-Detector

Disclaimer

Requirements

Installation

Data

Run

Setting the environment

Train

Eval

PCA versions of train / test

Known issues & contributions

License

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