AI_ 视频监控-人体移动捕捉监测

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我的各种github 开源项目和代码：https://github.com/linbin524

需求

为了实现特定场景中人员监控、人脸识别的需求，针对相关技术做研究。近场的动态人脸识别已经实现；现在需要针对人距离的移动人物进行捕捉截取，确定当前场所行走的人员做收集。

实现效果：

技术方案

1、采用Emgu CV 开源框架，对人体进行动态捕捉

2、介绍摄像头采集 识别移动人体模式

技术实现

动态截取人物

代码：

    void ProcessFrame(object sender, EventArgs e)
        {
            Mat frame = _cameraCapture.QueryFrame();
            Mat smoothedFrame = new Mat();
            CvInvoke.GaussianBlur(frame, smoothedFrame, new Size(, ), ); //filter out noises
                                                                            //frame._SmoothGaussian(3); 

            #region use the BG/FG detector to find the forground mask

            Mat forgroundMask = new Mat();
            _fgDetector.Apply(smoothedFrame, forgroundMask);
            #endregion

            CvBlobs blobs = new CvBlobs();
            _blobDetector.Detect(forgroundMask.ToImage<Gray, byte>(), blobs);
            blobs.FilterByArea(, int.MaxValue);

            float scale = (frame.Width + frame.Width) / 2.0f;
            _tracker.Update(blobs, 0.01 * scale, , );

            long detectionTime;

            List<Rectangle> faces = new List<Rectangle>();
            List<Rectangle> eyes = new List<Rectangle>();

            IImage image = (IImage)frame;//这一步是重点
            faceImage = frame.Bitmap;

            #region 人物识别
            long processingTime;
            Rectangle[] results;

            if (CudaInvoke.HasCuda)
            {
                using (GpuMat gpuMat = new GpuMat(frame))
                    results = FindPedestrian.Find(gpuMat, out processingTime);
            }
            else
            {
                using (UMat uImage = frame.GetUMat(AccessType.ReadWrite))
                    results = FindPedestrian.Find(uImage, out processingTime);
            }

            foreach (Rectangle rect in results)
            {
                CvInvoke.Rectangle(frame, rect, new Bgr(Color.Red).MCvScalar);
            }

            #endregion

            imageBox1.Image = frame;
            imageBox2.Image = forgroundMask;
        }

人物识别动态捕捉核心代码：

using System;
using System.Collections.Generic;
using Emgu.CV;
using Emgu.CV.CvEnum;
using Emgu.CV.Structure;
using System.Drawing;
using System.Diagnostics;
using Emgu.CV.Util;
#if !(__IOS__ || NETFX_CORE)
using Emgu.CV.Cuda;
#endif

namespace PedestrianDetection
{
   public static class FindPedestrian
   {
      /// <summary>
      /// Find the pedestrian in the image
      /// </summary>
      /// <param name="image">The image</param>
      /// <param name="processingTime">The processing time in milliseconds</param>
      /// <returns>The region where pedestrians are detected</returns>
      public static Rectangle[] Find(IInputArray image, out long processingTime)
      {
         Stopwatch watch;
         Rectangle[] regions;

         using (InputArray iaImage = image.GetInputArray())
         {
#if !(__IOS__ || NETFX_CORE)
            //if the input array is a GpuMat
            //check if there is a compatible Cuda device to run pedestrian detection
            if (iaImage.Kind == InputArray.Type.CudaGpuMat)
            {
               //this is the Cuda version
               using (CudaHOG des = new CudaHOG(new Size(, ), new Size(, ), new Size(, ), new Size(, )))
               {
                  des.SetSVMDetector(des.GetDefaultPeopleDetector());

                  watch = Stopwatch.StartNew();
                  using (GpuMat cudaBgra = new GpuMat())
                  using (VectorOfRect vr = new VectorOfRect())
                  {
                     CudaInvoke.CvtColor(image, cudaBgra, ColorConversion.Bgr2Bgra);
                     des.DetectMultiScale(cudaBgra, vr);
                     regions = vr.ToArray();
                  }
               }
            }
            else
#endif
            {
               //this is the CPU/OpenCL version
               using (HOGDescriptor des = new HOGDescriptor())
               {
                  des.SetSVMDetector(HOGDescriptor.GetDefaultPeopleDetector());
                  watch = Stopwatch.StartNew();

                  MCvObjectDetection[] results = des.DetectMultiScale(image);
                  regions = new Rectangle[results.Length];
                  for (int i = ; i < results.Length; i++)
                     regions[i] = results[i].Rect;
                  watch.Stop();
               }
            }

            processingTime = watch.ElapsedMilliseconds;

            return regions;
         }
      }
   }
}

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