This OpenCV tutorial is a very simple code example of GPU Cuda optical flow in OpenCV written in c++. The configuration of the project, code, and explanation are included for farneback Optical Flow method. Farneback algorithm is a dense method that is used to process all the pixels in the given image. The dense methods are slower but more accurate as all the pixels of the image are processed. In the following example, I am displaying just a few pixes based on a grid. I am not displaying all the pixes. In the opposite to dense method the sparse method like Lucas Kanade using just a selected subset of pixels. They are faster. Both methods have specific applications. Lucas-Kanade is widely used in tracking. The farneback can be used for the analysis of more complex movement in image scene and furder segmentation based on these changes. As dense methods are slightly slower, the GPU and Cuda implementation can lead to great performance improvements to calculate optical flow for all pixels o
I just performed people detection by Yolo model in Opencv 4 DNN module. I analyzed my old dataset, which was used for the same task performed by Haar, LBP as well.
I will let the evaluation on your visual feeling. Let me know if there is an application out there building on HAAR, LBP. I think that even the cameras head focus/tracking systems are no more based on HAARs. Your comments are welcomed on facebook.
YOLO DARKNET (CPU performance) people detection in opencv 4
300 ms per image- width=316 --height=510 --scale=0.00392
That's cool
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