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

The blog is full of OpenCV source code, tutorials, tips, tricks, and from basics to advanced streaming and video processing. The code examples are from C++. Most of the tutorials are dedicated to basics C++ OpenCV image processing, people detection from LBP haar cascades to modern deep learning. The tutorials are as well dealing with GStreamer OpenCV integration to be able to stream OpenCV output as a video stream to the web.