使用Kinect的手指/手势识别 [英] Finger/Hand Gesture Recognition using Kinect
问题描述
让我解释我的需要，然后我解释问题。
我期待着一个手控应用程序。
使用手掌和点击使用抓取/拳头导航。
Let me explain my need before I explain the problem. I am looking forward for a hand controlled application. Navigation using palm and clicks using grab/fist.
目前，我正在与Openni合作，这听起来很有前途，并且有几个例子，在我的情况下，因为它已经在样品中建立手动跟踪仪。
Currently, I am working with Openni, which sounds promising and has few examples which turned out to be useful in my case, as it had inbuild hand tracker in samples. which serves my purpose for time being.
我想问的是，
1）
我训练和使用Adaboost拳头分类器对提取的RGB数据，这是相当不错，
I trained and used Adaboost fist classifiers on extracted RGB data, which was pretty good, but, it has too many false detections to move forward.
2）是否还有其他好的图书馆能够使用深度数据实现我的需求？
3）自己的手势，特别是使用手指，因为一些纸是指HMM，如果是，我们如何继续使用像OpenNI这样的库？
我试着在OpenNI的中间仓库图书馆喜欢，抓斗检测器，但是，他们不服务我的目的，因为它既没有开源也不符合我的需要。
Yeah, I tried with the middle ware libraries in OpenNI like, the grab detector, but, they wont serve my purpose, as its neither opensource nor matches my need.
推荐答案
如果你有什么想法可以帮助我，不需要训练你的第一个算法，因为它会使事情复杂化。
不要使用颜色，因为它不可靠（根据照明和视角混合背景和不可预测的变化）
You don't need to train your first algorithm since it will complicate things. Don't use color either since it's unreliable (mixes with background and changes unpredictably depending on lighting and viewpoint)
- 你的手是一个最接近的对象，你可以简单地
将其按深度阈值分段。您可以手动设置阈值，使用最接近的深度直方图区域，或执行连接组件在深度图上，首先将其分解为有意义的部分（然后不仅根据其深度，而且使用其尺寸，运动，用户输入等来选择您的对象）。下面是连接组件方法的输出：
- Assuming that your hand is a closest object you can simply segment it out by depth threshold. You can set threshold manually, use a closest region of depth histogram, or perform connected component on a depth map to break it on meaningful parts first (and then select your object based not only on its depth but also using its dimensions, motion, user input, etc). Here is the output of a connected components method:
Apply convex defects from opencv library to find fingers;
跟踪手指而不是手指，而不是从头到尾重新发现他们在3D.This将增加稳定性。我成功地实现了这种手指检测大约3年前。
Track fingers rather than rediscover them in 3D.This will increase stability. I successfully implemented such finger detection about 3 years ago.
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