问题描述
我发现了此教程，该教程创建了自己的haar-classifier级联.
I found this tutorial on creating your own haar-classifier cascades.
这给我提出了一个问题:运行HaarTraining和创建自己的分类器(与使用OpenCv提供的级联相对)有什么好处(如果有的话)?
This raised the question with me: what are the advantages, if any, of running HaarTraining, and creating your own classifier (as opposed to using the cascades provided by OpenCv)?
推荐答案
Haar或LBP级联分类器是用于检测物体或刚性物体的常用技术.因此，这是训练您自己的级联的两个主要要点:
Haar or LBP cascades classifiers are common technique used for detection or rigid objects. So here are two major points for training your own cascade:
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OpenCV附带的级联并未涵盖所有可能的对象类型.因此，如果要创建面部检测应用程序，则可以使用OpenCV级联之一，但是如果需要检测例如狗，则没有准备使用级联的功能.
Cascades coming with OpenCV do not cover all possible object types. So you can use one of OpenCV cascades if you are going to create a face-detection application but there no ready to use cascades if you need to detect for example dogs.
来自OpenCV的级联很好，但并不是最好的级联.这是一项艰巨的任务，但有可能训练级联，使其具有更高的检测率，并产生较少的假阳性和假阴性.
And cascades from OpenCV are good but they are not the best possible. It is a challenging task but it is possible to train a cascade that will have higher detection rate and produce less false-positives and false-negatives.
还有一个重要的说明:您的教程中使用的haartraining应用程序现在被OpenCV团队视为已弃用. opencv_traincascade是较新的版本，它具有2个重要功能:它支持LBP功能，并且支持多线程(TBB).典型的区别是这样的
And one major remark: haartraining application used in your tutorial is now considered as deprecated by OpenCV team. opencv_traincascade is a newer version and it has 2 important features: it supports LBP features and it supports multi-threading (TBB). Typical difference looks like this
haartraining + singlecore> 3周为一个分类器.
级联+多核<一个分类器30分钟.
haartraining + singlecore > 3 weeks for one classifier.
traincascades + multicore < 30 minutes for one classifier.
但是最糟糕的是，我不知道任何解释opencv_traincascade用法的优秀教程.有关详细信息，请参见此线程.
But the worst of all I don't know any good tutorials explaining usage of opencv_traincascade. See this thread for details.
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