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Comment by seesawtron | Hacker News Reader

Does anyone in this field of "flow" based segmentation know how would the performance be if there were multiple moving objects in the same scene? The examples in this paper seem limted to one moving object (cluster of moving pixels) apposed to a relatively static background?

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In terms of accuracy, the authors mention it as a limitation, so probably it could be a problem.

In terms of runtime, it should not matter. Generally speaking though, the overhead of optical flow is often overlooked. For video DL applications, optical flow calculation often takes more time than inference itself. For academic purposes, datasets are often already preprocessed and the optical flow runtime is not mentioned. Doing real time video analysis with optical flow is quite impractical though.

The time for computing optical flow is indeed a bottleneck, and some researchers in the community is working on that: - https://arxiv.org/abs/2103.04524 - https://arxiv.org/pdf/2103.17271.pdf

Its an active field of research. Optical flow algorithms are typically used to create vector fields and have shown promise in fluid flows (https://www.mdpi.com/2072-4292/13/4/690/htm). However a deep learning version of this on real life data has yet to be accomplished. If anyone reading this knows otherwise plz link :D

I'm not sure what using the vectors for fluids has to do with this or what the parent asked. Fluids can use vectors, optical flow can produce vectors, but that is about it.

This person asked about clustering and segmenting into more than two separate groups.

Yeah, and this paper describes both phenomena combined.

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