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
Multiple people tracking
Opencv tracking of multiple people example video and some ideas about the business behind..
I would like to mention that this video is not related on my any curent business. This is just love to computer vision.
This short blog post should be about something else.
Problem of computer vision business potencial
- People point to any mistake of algorithm. Mistakes are visible. People dont care if the big data gives you huge statistics error. They are not able to count the milions line of something.
- They are able to count what they are really see on video.
Where is the point. Even the error is visible and sometines huge. This gives you better result than one man counting the people from the window all day long.
Are you able to track and monitor what every on screen doing and count targets and write the results somewhere.
No
Do not sell video. Sell the data
This is important. Maybe the most important think. You are provide data about the day activity about the year activity what ever.
Not the person for one day counting of the trafic on the Prague town center crossing.
Video delivery or internet of things
You have to solve the problem with stream video delivery to cloud as cheap as it posible.
There is one think. Internet of thinks. Maybe is better old aproach just manage the computer vision where the camera is located.. Computers are smaller and faster. The video delivery is problem.. You have to deal with so many problems. Insted of transfering just the data.
Benefits are that the people infront of the monitor just see your mistakes.
Cost effective computer vision
There is maybe question what is effective. Process the video in cloud has also many advantages. Scales. Updates. Your algorithm is under control. You can handle what ever resolution. This is only issue of the cost of your solution. It is not necesary with some cameras to manage your own HW in that solution.
I have a lots of idea like that. I spent lots of time not only on algorithm itself. For example another huge topic is
Self configuration of computer vision algorithm
Yes this one. Not a spend 3 hours or months to make perfect youtube video. There is no reason.
Algorithm should be somehow self configurating. And it may not be perfect. Let the rest of the world accept error also in computer vision.
Have a nice evening and let me know. What do you think.
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