Contents
What is optical flow used for?
Optical flow, or motion estimation, is a fundamental method of calculating the motion of image intensities, which may be ascribed to the motion of objects in the scene. Optical flow is an extremely fundamental concept that is utilized in one form or another in most video-processing algorithms.
Why is my optical flow not rendering?
FCPx wont render ANY clips with Optical Flow IF that clip has an auto Speed Ramp applied to it… Remove the speed ramp and it works. FCPx will render optical flow if the clip is “just” retimed as a whole… But NOT if you have made an auto speed ramp from the Retime Menu… Above is a known and reported bug.
What is the main idea of optical flow?
Optical flow is a per pixel prediction and the main idea is that it assumes a brightness constancy, meaning it tries to estimate how the pixels brightness moves across the screen over time.
What does flownetc stand for in optical flow?
FlowNetC which stands for Flownet Correlated. It was originally introduced in the FlowNet 2015 paper, it was meant to be an improvement on FlowNetS which had high EPE compared to traditional optical flow implementations. A visual summary of the algorithm is as follows:
What is optical flow and why does it matter in deep learning?
FlowNetCSS was introduced in the FlowNet 2.0 paper and the main idea behind it is that optical flow estimations can be greatly improved by stacking the networks. A second stream of the network contains FlowNetSD which is fed with the original Image 1 and Image 2. The FlowNetSD network stands for FlowNet Small Displacements.
How is the loss function for optical flow calculated?
The loss function for calculating optical flow is done by making use of the end point error. This is shown diagrammatically as: The end-to-end point error is calculated by comparing the predicted optical flow vector with the ground truth (expected) optical flow vector, and is the Euclidean distance between the two vectors.