使用OpenCV的物体检测和隔离 [英] Object detection and isolation using OpenCV

问题描述

我一直使用的OpenCV与以发现不同的身体部位（手，胳膊，脸，e.t.c）相当数量的成功启动。

I've started using OpenCV with a fair amount of success with a view to detecting different body parts (hands, arms, face, e.t.c).

第一个也是最明显的方法来实现这一目标是使用哈尔或LBP级联分类，但我想知道还有什么其他的选择都在那里。

The first and most obvious approach to achieve this is using Haar or LBP cascade classifiers, but I would like to know what other options are out there.

我见过用色范围内HSV图像和探测像素皮肤检测的例子。但是，这并不能帮助确定哪些位是！我还实现了一个MOG2背景减除它可以帮助找出一个移动的物体，但如果所需的对象是只有一个运动这只会工作。

I've seen examples of skin detection using HSV images and detecting pixels within a color range. But this does not help determine which bit is which! I've also implemented an MOG2 background subtraction which can help isolate a moving object but this would only work if the desired object is the ONLY one moving.

利用凸包和缺陷，我概括的手和胳膊，但两者之间不能区分。是否有这样做的标准方法或正在使用级联分类的唯一可靠的方法？

Using convex hull and defects I've outlined hands and arms but cannot distinguish between the two. Is there a standard way of doing this or is the only reliable way using cascade classifiers?

推荐答案

您可以尝试 HOG描述符其中凡在2006年开发的。
他们的工作非常好面孔和行人。他们的工作不是单一的身体部位（仍比Haar特征更好）好。
他们被认为是最先进的国家，直到深度学习上来。 （2012年左右，我认为）

You can try HoG descriptors which where developed in 2006. They work very well for faces and pedestrians. They work not that good for single body parts (still better than haar features). They were considered as state of the art until deep learning came up. (around 2012 I think)

在此<一个描述的系统href=\"https://www.cs.berkeley.edu/~rbg/papers/Object-Detection-with-Discriminatively-Trained-Part-Based-Models--Felzenszwalb-Girshick-McAllester-Ramanan.pdf\">paper多次获得competetions当它是presented。

The system described in this paper won several competetions when it was presented.

生猪在OpenCV的实现，但 DLib 实施比较好。

Hog is implemented in OpenCV but the DLib implementation is better.

另请参阅这对于很多对一般计算机视觉有用的信息。

See also this for a lot of usefull information about computer vision in general.

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