其适用于轿厢检测，CascadeClassifier或HOGDescriptor？ [英] Which is suitable for car detection, CascadeClassifier or HOGDescriptor?

问题描述

我的目标是检测图像的汽车，并承认它的模型。对于汽车检测，从 http://docs.opencv.org/trunk/opencv_cheatsheet.pdf，它说：

My goal is to detect cars in images and recognize its model. For car-detection, from http://docs.opencv.org/trunk/opencv_cheatsheet.pdf, it says:

CascadeClassifier

CascadeClassifier

提振分类美荷兰国际集团哈尔或LBP特征的中提琴的级联。   西服去tecting脸，面部特征和其他一些物品   没有多样化的纹理。见facedetect.cpp

Viola’s Cascade of Boosted classifiers us-ing Haar or LBP features. Suits for de-tecting faces, facial features and some other objects without diverse textures. See facedetect.cpp

HOGDescriptor

HOGDescriptor

ñ。用柱状图导向-梯度（HOG）达拉尔的对象检测器   特征。对于西服检测 - 荷兰国际集团的人，车等物体   定义良好的剪影。见peopledetect.cpp

N. Dalal’s object detector using Histogram-of-Oriented-Gradients (HOG) features. Suits for detect-ing people, cars and other objects with well-defined silhouettes. See peopledetect.cpp

和从 http://docs.opencv.org/opencv2refman.pdf ，第8.1章级联分类，421页：

and from http://docs.opencv.org/opencv2refman.pdf , chapter 8.1 Cascade Classification ,page 421:

首先，将分类器（升压分类器即级联工作   与类Haar特征）进行训练的几百次样品   特定对象（即，人脸或汽车），所谓的正面例子，   该被调整为相同的大小（例如，20×20），和负的例子 -   任意图像的大小相同的

First, a classifier (namely a cascade of boosted classifiers working with haar-like features) is trained with a few hundred sample views of a particular object (i.e., a face or a car), called positive examples, that are scaled to the same size (say, 20x20), and negative examples - arbitrary images of the same size.

这两种两种方法提及关于申请汽车检测：在opencv_cheatsheet.pdf它说HOGDescriptor西服汽车和opencv2refman.pdf也提到了汽车的级联分类。所以我的问题是，如何CascadeClassifier和HOGDescriptor之间进行选择，而这对车的检测好？谢谢你。

Both of these two methods mention about applying for car detection: in opencv_cheatsheet.pdf it says HOGDescriptor suits for cars and in opencv2refman.pdf it also mentions cars for Cascade Classification. So my question is how to choose between CascadeClassifier and HOGDescriptor, and which is better for car detection? Thank you.

推荐答案

它很难说没有测试，但实际上我得到了第三个选项非常好的效果 - latentSVM探测器。

Its hard to tell without testing, but actually I got very good results with a third option - latentSVM detector.

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