Helping on stackoverflow with tracking and people counting.

Follow

My favourite

  • Opencv tutorial people detection
  • Head people cascade download
  • Opencv tutorial optical flow
  • Opencv Video stabilization
  • Opencv car dataset download
  • Opencv tutorial Transparent mask
  • Opencv videowriter
  • Opencv FFMPEG
  • Opencv Canny edge and hough lines
  • Helping on stackoverflow

    Maybe some of this ideas are useful also for you. 

    Question was?? 

    You have one people income stream and you need to determine exit points, Right, Left or just straight. Use detection and statistic or whole tracking.


    tracking is best to solve this problem, I think


    This is my ansfer.
    The best accurate way is to use tracking algorithm instead of statistic appearance counting of incoming  people and detection occurred left right and middle..
    You can use extended statistical models.. That produce how many inputs producing one of the outputs and back validate from output detection the input.

    People detection


    My experience is that tracking leads to better results than approach above. But is also little bit complicated. We talk about multi target tracking when the critical is match detection with tracked model which should be update based on detection. If tracking is matched with wrong model. The problems are there.
    [![enter image description here][1]][1]

    Here on youtube I developed some multi target tracker by simple LBP people detector, but multi model and kalman filter for tracking. Both capabilities are available in opencv. You need to when something is detected create new kalman filter for each object and update in case you match same detection. Predict in case detection is not here in frame and also remove the Kalman i it is not necessary to track any more.
    1 Detect
    2 Match detections with kalmans, hungarian algorithm and l2 norm. (for example)
    3 Lot of work. Decide if kalman shoudl be established, remove, update, or results is not detected and should be predicted. This is lot of work here.
    Pure statistic approach is less accurate, second one is for experience people at least one moth of coding and 3 month of tuning.. If you need to be faster and your resources are quite limited. You can by smart statistic achieve your results by pure detection much faster and little bit less accurate. People are judge the image and video tracking even multi target tracking is capable to beat human. Try to count and register each person in video and count exits point. You are not able to do this in some number of people. It is really repents on, what you want, application, customer you have, and results you show to customers. If this is 4 numbers income, left, right, middle and your error is 20 percent is still much more than one bored small paid guard should achieved by all day long counting..


    You can find on my BLOG Some dataset for people detection and car detection on my blog same as script for learning ideas, tutorials and tracking examples..
    [Opencv blog tutorials code and ideas][2]



      [2]: https://funvision.blogspot.com/

    No comments:

    Post a Comment

    ad