Which method for object detection at 25 fps, full HD?

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@kbarni, you are right, sorry. I do have my own Mask R-CNN model working offline (separate still frames).

• I want to detect moving road signs filmed from a car with a HD TV camera. The bounding boxes should be filled with a solid color and moving as smoothly as possible. The video output is full HD TV broadcast quality 25 fps.

• Right now I have a laptop with 2GB GPU, but I can perhaps ask for a workstation with a decent GPU. I thought OpenCV did not support Cuda?

• I have to make all the training data myself, right now I have only a handful of labels. Those labels were enough for a simple test of the Mask model.

I still don't understand why do you need broadcast quality output? You want to run a TV show with traffic signs? Normally you should be only interested what kind of sign is near the road...

Thanks for asking @kbarni! If I can get a proof of concept running, the solution might eventually be shown on TV. The input will be a live HD video feed; the model should detect road signs and replace them with colors; the output will be a HD video feed for TV, delayed by maximum 5 seconds compared to input. I am sorry I can't disclose more details.

I do not need pixel-precise segmentation or advanced polygons by the way. Rectangular bounding boxes with 4 corners will be sufficient. I expect quite a bit of zooming and camera pan, so the objects could move relatively fast.

Okay, now you are getting clearer. So: - You don't need masks, just bounding boxes! - You don't need HD quality output, just bounding boxes drawn on a HD image.

I suggest to use YoloV3; it is very fast (much faster than Mask-RCNN) and runs real-time on GPUs or nvidia Tegra cards. If you prefer OpenCV, it can take cv::Mat images as input (otherwise just pass the memory buffer). Then you can draw the results on the HD image:

Detector det("network.cfg","network.weights");
std::vector<bbox_t> markers;
markers = det.detect(img);
for(size_t i=0; i < markers.size(); i++) {
    if(markers[i].prob > 0.5)
        rectangle(img,Point2i(markers[i].x, markers[i].y), Point2i(markers[i].x + markers[i].w, markers[i].y + markers[i].h), Scalar(0,0,255), 5);
}

Thank you so much! I will look into YoloV3 now, starting with Learn OpenCV