|Trend||Dataset||Best Method||Paper title||Paper||Code||Compare|
Dynamics of human body skeletons convey significant information for human action recognition.
#2 best model for Skeleton Based Action Recognition on Varying-view RGB-D Action-Skeleton
The other contribution is our study on a series of good practices in learning ConvNets on video data with the help of temporal segment network.
#3 best model for Multimodal Activity Recognition on EV-Action
We present the Moments in Time Dataset, a large-scale human-annotated collection of one million short videos corresponding to dynamic events unfolding within three seconds.
In this work, we propose to use a new class of models known as Temporal Convolutional Neural Networks (TCN) for 3D human action recognition.
We report state-of-the-art or comparable results on video action recognition on the largest multimodal dataset available for this task, the NTU RGB+D, as well as on the UWA3DII and Northwestern-UCLA.
Human action recognition remains as a challenging task partially due to the presence of large variations in the execution of action.
While EMG is used as an effective indicator for biomechanics area, it has yet to be well explored in multimedia, computer vision, and machine learning areas.
#2 best model for Multimodal Activity Recognition on EV-Action