AI_ 视频监控-人体移动捕捉监测

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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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