Tracking people in video with calculating the average speed of the monitored points

This example shows a new method for tracking significant points in video, representing people or moving objects. This method uses several OpenCV functions.

The process

1. The opening video file

   [c language=”c++”]
   VideoCapture MojeVideo („cesta k súboru");
   [/c]

2. Retrieve the next frame (picture)

   [c language=”c++”]
   Mat FarebnaSnimka;
   MojeVideo >> FarebnaSnimka;
   [/c]

3. Converting color images to grayscale image

   [c language=”c++”]
   Mat Snimka1;
   cvtColor(FarebnaSnimka, Snimka1, CV_RGB2GRAY);
   [/c]

4. Getting significant (well observable) points

   [c language=”c++”]
   vector<cv::Point2f> VyznacneBody;
   goodFeaturesToTrack(Snimka1, VyznacneBody, 300, 0.06, 0);
   [/c]

5. Getting the next frame and its conversion
6. Finding significant points from the previous frame to the next

   [c language=”c++”]
   vector<cv::Point2f> PosunuteBody;
   vector<uchar> PlatneBody;
   calcOpticalFlowPyrLK(Snimka1, Snimka2, VyznacneBody, PosunuteBody, PlatneBody, err);
   [/c]

7. Calculation of the velocity vector for each significant point
8. Clustering of significant points according to their average velocity vectors
   
9. Visualization
   1. Assign a color to cluster
   2. Plotting points on a slide
   3. Plotting arrows at the center points of clusters – average of the average velocity vectors
10. Dumping the clusters and other places for the classification of points into them (to preserve the color of the cluster) + eventual creation of new clusters
11. Landmarks declining over time – the time when they need to re-designate

Result

• This method is faster than OpenCV method for detecting people.
• It also works when only part of person is visible, position is unusual or person is rotated.
• Person is divided to parts.
• It does not distinguish between persons or other moving objects.