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
Car detection video samples
This is one of the results achieved by the free dataset for car detection on my blog here. I have a plan to provide some basic scripts and code samples how to learn the basic detector for opencv. This usually take some time to go through and describe all the parts. However the plan is to provide whole video for testing on google drive after some anonymization improvements in original video. Testing rof car counting, classification and trafic measurement.. This Video is not good for basic background substraction. I record this video by hand and is still little bit shaky.
Superbly written article, if only all bloggers offered the same content as you, the internet would be a far better place..
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Thank for your help..I will hope help you
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