3 ms·
>As a practical example, figuring out where a given pixel moves from one video frame to the next one, when working on real-world videos, the best known algorith
by throwlaplace 7y ago
>As a practical example, figuring out where a given pixel moves from one video frame to the next one, when working on real-world videos, the best known algorithms get about 50% of the pixels correct. With clever filtering, you can maybe bump that to 60 or 70%, but in any case you will be left with a 30%+ error rate.
what do you mean by this? optical flow isn't really a learning problem? it's a classical problem with very good classical algorithms
https://www.mia.uni-saarland.de/Publications/brox-eccv04-of.pdf https://www.mia.uni-saarland.de/Publications/brox-eccv04-of....
https://people.csail.mit.edu/celiu/OpticalFlow/ https://people.csail.mit.edu/celiu/OpticalFlow/
https://github.com/pathak22/pyflow https://github.com/pathak22/pyflow
- fxtentacle 7y agoIt used to be. Then the AI fanboys arrived and started treating it like a learning problem. https://arxiv.org/abs/1612.01925 https://arxiv.org/abs/1612.01925 https://arxiv.org/abs/1709.02371 https://arxiv.org/abs/1709.02371 https://arxiv.org/abs/1904.09117 https://arxiv.org/abs/1904.09117 BTW, also the classical algorithms deal very badly with noise and repetitive textures, e.g. a video of a forest in the afternoon.
- rsp1984 7y agoEver tried "DIS optical flow" in OpenCV? Works like a charm for me even in challenging conditions.
- fxtentacle 7y agoNot yet, thanks for the suggestion :)