Jun 21, 2020 · In this paper, we propose a fast but effective way to extract motion features from videos utilizing residual frames as the input data in 3D ConvNets.
In this paper, we propose an effective strategy based on. 3D convolutional networks to pre-process RGB frames for the generation and replacement of input data.
Analysis shows that better motion features can be extracted using residual frames compared to RGB counterpart, and this proposal can be even better than ...
By replacing traditional stacked RGB frames with residual ones, 35.6% and 26.6% points improvements over top-1 accuracy can be obtained on the UCF101 and HMDB51 ...
MOTION REPRESENTATION USING RESIDUAL FRAMES WITH 3D CNN. Collection: IEEE ... US $15.00. Purchase. Videos in this product. MOTION REPRESENTATION USING RESIDUAL ...
By replacing traditional stacked RGB frames with residual ones, 35.6% and 26.6% points improvements over top-1 accuracy can be achieved on the UCF101 and HMDB51 ...
Missing: CNN. | Show results with:CNN.
Residual frames with 3D ConvNets have been proved to be more effective compared to original RGB video clips [37], [88] , which can also boost the performance in ...
Further analysis indicates that better motion features can be extracted using residual frames with 3D ConvNets, and our residual-frame-input path is a good ...
Missing: CNN. | Show results with:CNN.
Recently, 3D convolutional networks (3D ConvNets) yield good performance in action recognition. However, optical flow stream is still needed to ensure ...
We deeply analyze the effectiveness of this modality compared to normal RGB video clips, and find that better motion features can be extracted using residual ...