A Self-supervised Attention Mechanism To Help With Dense Optical Flow Estimationby@kraken

A Self-supervised Attention Mechanism To Help With Dense Optical Flow Estimation

tldt arrow
Read on Terminal Reader

Too Long; Didn't Read

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.

Companies Mentioned

Mention Thumbnail
Mention Thumbnail

Coin Mentioned

Mention Thumbnail
featured image - A Self-supervised Attention Mechanism To Help With Dense Optical Flow Estimation
Rishab Sharma HackerNoon profile picture

@kraken

Rishab Sharma

About @kraken
LEARN MORE ABOUT @KRAKEN'S EXPERTISE AND PLACE ON THE INTERNET.
react to story with heart

RELATED STORIES

L O A D I N G
. . . comments & more!
Hackernoon hq - po box 2206, edwards, colorado 81632, usa