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Dense Optical Flow Estimation is based on Gunner Farneback’s algorithm which is explained in “Two-Frame Motion Estimation Based on Polynomial Expansion” by Gunner. In this blog, we will discuss how this kind of dense tracking approach is achieved through self-supervised attention mechanisms. OpenCV provides the code function to this algorithm to find the dense optical flow. Dense optical flow computes one optical flow vector per pixel for every frame in the video sequence. This approach gives a more suitable output for applications such as video segmentation and structural learning from motion.