手部检测和追踪方法 [英] Hand detection and tracking methods

问题描述

所以，伙计们，请帮助我为坐在计算机（便携式）前置摄像头前面的计算机的用户检测/跟踪手。
我尝试了以下方法：

• 基于颜色的检测（我检测到了人类通过opencv haar级联进行人脸检测并提取了皮肤的HSV范围。在下一个中，我找到了具有肤色的对象。例如，我可以通过haar级联进行人脸检测来移除人脸，但是其他人体呢？
  
• 训练自己的haar级联分类器（我我用3.5k的正照片和4k的负照片训练了我自己的级联来检测手。花了3天的时间进行训练。数据集非常丰富（各种手的配置和方向，光照条件，背景不同）。因为我将 scaleFactor = 1.3 和 minNeighbors = 70 设置得非常慢，所以如果我减小 minNeighbors 虚假警报将大大增加并且反应很小角度将覆盖整个视频帧。
  训练参数：
  opencv_traincascade-数据数据-vec samples.vec -bg neg.txt -numStages 16 -minhitrate 0.999 -maxFalseAlarmRate 0.5 -numPos 3200 -numNeg 3900 -w 24- h 24模式ALL -precalcValBufSize 1024`` -precalcIdxBufSize 1024



• 训练LBP级联分类器（训练速度更快比haar级联和检测更接近实时，但是此检测方法有很多误解）训练参数： opencv_traincascade -data lbp -vec samples.vec -bg neg.txt -numStages 25 -minHitRate 0.999 -maxFalseAlarmRate 0.5 -numPos 3200 -numNeg 3900 -w 24 -h 24-模式ALL -precalcValBufSize 4096 -precalcIdxBufSize 4096 -featureType LBP 我尝试了numStages的不同值，从16到25。




• Camshift跟踪手的算法源代码在此处

  
  
  

  要训练神经网络，请遵循以下说明，也可以从中获取二进制文件此处。

  
  

  So, guys, please help me with detecting/tracking hand for user who are sitting at the computer in front of computer(laptop) frontal camera. I've tried these methods:

  • Colour based detection(I've detected the human face by opencv haar cascade face detection and extracted the skin HSV ranges. In the next I've found the objects with the skin colour. For example, the face I can remove by knowing face detection by haar cascade, but what about other human body parts and background objects with skin colour if I need only hand? How to make this algorithm be more stable for illumination?)
  • Train own haar cascade classifier(I've trained my own cascade to detect hand using 3.5k positive and 4k negative photos. It took 3 days to train. The dataset is pretty rich(various hand configurations and orientations, light conditions, different backgrounds). It works not so bad but it's very slow because of I set scaleFactor=1.3 and minNeighbors=70. If I decrease minNeighbors false alarms will grow tremendously and small reactangles will cover the whole video frame. Training params: opencv_traincascade -data data -vec samples.vec -bg neg.txt -numStages 16 -minhitrate 0.999 -maxFalseAlarmRate 0.5 -numPos 3200 -numNeg 3900 -w 24 -h 24 -mode ALL -precalcValBufSize 1024`` -precalcIdxBufSize 1024

  • Train LBP cascade classifier (The training was faster than haar cascade and detection works closer to real time but this detection method has a lot mishits) Training params: opencv_traincascade -data lbp -vec samples.vec -bg neg.txt -numStages 25 -minHitRate 0.999 -maxFalseAlarmRate 0.5 -numPos 3200 -numNeg 3900 -w 24 -h 24 -mode ALL -precalcValBufSize 4096 -precalcIdxBufSize 4096 -featureType LBP I tried the different values of numStages from 16 to 25.

  • Camshift algorithm to track hand The source code is here http://pastebin.com/q5zK8cZt. How it works? Just need to mark 4 poins around detected object and this algorithm must track it and draw rectangle around. The problem is if I started to move my hand this rectangle starts to grow and cover the whole video frame. It looks like this algorithm works only for small objects (or the objects are locating long distance from camera)

  Maybe I need to mix these methods or you will suggest another? Maybe I need to train neural network for example YOLO? I don't have wish to do it cause of it takes too long time and have to rent GPU-based servers.

  解决方案

  GPU servers? No, you don't: there is a web based backend for object recognition. If you want to use Yolo you will need to mark a huge image train set (Around 2000 per class). I can advise fetch images from here using script like that

  (function(global) {
    const next = () => Array.from(document.querySelectorAll('.search-pagination__button-text'))[1].click();
    const uuid = () => Math.random().toString(36).substring(7);
    const sleep = (timeout = 5000) => new Promise((res) => setTimeout(() => res(), timeout));
    global.urls = [];
    global.next = () => next();
    global.start = async () => {
      for (let i = 0; i !== 81; i++) {
          window.scrollTo(0,document.body.scrollHeight);
          await sleep(5000);
          document.querySelectorAll('.search-content__gallery-results figure > img[src]').forEach(({src}) => global.urls.push(src));
          next();
          await sleep(5000);
      }
    };
  })(window);
  

  After that, you need to mark bounded boxes of objects in images. There is a online tool, which work right in your web browser

  For training neural network follow this instruction, binaries also can be taken from here.

  这篇关于手部检测和追踪方法的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！