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• Detect bad frames in OpenCV 2.4.9

  14 mai 2014, par user3630380

  I know the title is a bit vague but I’m not sure how else to describe it.

  CentOS with ffmpeg + OpenCV 2.4.9. I’m working on a simple motion detection system which uses a stream from an IP camera (h264).

  Once in a while the stream hiccups and throws in a "bad frame" (see pic-bad.png link below). The problem is, these frames vary largely from the previous frames and causes a "motion" event to get triggered even though no actual motion occured.

  The pictures below will explain the problem.

  Good frame (motion captured) :

  

  Bad frame (no motion, just a broken frame) :

  

  The bad frame gets caught randomly. I guess I can make a bad frame detector by analyzing (looping) through the pixels going down from a certain position to see if they are all the same, but I’m wondering if there is any other, more efficient, "by the book" approach to detecting these types of bad frames and just skipping over them.

  Thank You !

  EDIT UPDATE :

  The frame is grabbed using a C++ motion detection program via cvQueryFrame(camera); so I do not directly interface with ffmpeg, OpenCV does it on the backend. I’m using the latest version of ffmpeg compiled from git source. All of the libraries are also up to date (h264, etc, all downloaded and compiled yesterday). The data is coming from an RTSP stream (ffserver). I’ve tested over multiple cameras (dahua 1 - 3 MP models) and the frame glitch is pretty persistent across all of them, although it doesn’t happen continuously, just once on a while (ex : once every 10 minutes).

• libavfilter/vf_dnn_detect : Add yolo support

  21 novembre 2023, par Wenbin Chen

  libavfilter/vf_dnn_detect : Add yolo support
  Add yolo support. Yolo model doesn't output final result. It outputs
  
  candidate boxes, so we need post-process to remove overlap boxes to
  
  get final results. Also, the box's coordinators relate to cell and
  
  anchors, so we need these information to calculate boxes as well.
  Model detail please refer to : https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolo-v2-tf
  Signed-off-by : Wenbin Chen <wenbin.chen@intel.com>
  
  Reviewed-by : Guo Yejun <yejun.guo@intel.com>
  

  • [DH] libavfilter/dnn/dnn_backend_openvino.c
  • [DH] libavfilter/vf_dnn_detect.c

• libavfilter/vf_dnn_detect : Add input pad

  12 décembre 2023, par Wenbin Chen

  libavfilter/vf_dnn_detect : Add input pad
  Add input pad to get model input resolution. Detection models always
  
  have fixed input size. And the output coordinators are based on the
  
  input resolution, so we need to get input size to map coordinators to
  
  our real output frames.
  Signed-off-by : Wenbin Chen <wenbin.chen@intel.com>
  
  Reviewed-by : Guo Yejun <yejun.guo@intel.com>
  

  • [DH] libavfilter/dnn/dnn_backend_openvino.c
  • [DH] libavfilter/vf_dnn_detect.c