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Real-time inference? #9
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DIS optical flow has already been integrated into opencv, you can give it a try, for images not too large, the realtime performance seems pretty good. |
The operation points as described in the paper show that real-time
performance is easily within range of the existing code. But it largely
depends on the required accuracy. The OpenCV implementation (which I have
not written or tested) seems to achieve that as well.
The main computational bottleneck of this method in general (this repo or
OpenCV) is the computation of X and Y gradients for both images. If the
gradients are computed only once for each frame, as opposed to once for a
frame-pair as in the current code, large speed-ups are possible. The
spatial gradient code itself can easily be sped-up as well.
Relatedly, this algorithm has repeatedly been re-implement by corporate
partners, occasionally with GPU usage: One the worlds best well known
video/photo-story app is using this algorithm for live virtual augmentation
of video-feeds.
…On Sun, Jan 24, 2021 at 01:35 vitusleng ***@***.***> wrote:
DIS optical flow has already been integrated into opencv, you can have a
try, for images no too large, the realtime performance seems pretty good.
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Hi, I congrats for your work.
I would like to know whether at inference time is it possible to use this model as part of a framework that performs the online action detection task? (i.e. let's suppose I have an input stream video, is the model able to compute optical-flow features as the frames arrive, with real-time speed ?)
Thank you!
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