Image: Rahmani et al
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To achieve this, we model various exploratory inspection and diagnostic tasks for deep learning training processes as specifications for streams using a map-reduce paradigm with which many data scientists are already familiar.
Recent approaches in depth-based human activity analysis achieved outstanding performance and proved the effectiveness of 3D representation for classification of action classes.
In this work, we propose to use a new class of models known as Temporal Convolutional Neural Networks (TCN) for 3D human action recognition.
Ranked #1 on Multimodal Activity Recognition on EV-Action
Each available 3DV voxel intrinsically involves 3D spatial and motion feature jointly.
The proposed representation has the advantage of combining the use of reference joints and a tree structure skeleton.
Ranked #4 on Action Recognition on NTU RGB+D 120
Due to the availability of large-scale skeleton datasets, 3D human action recognition has recently called the attention of computer vision community.
Ranked #5 on Action Recognition on NTU RGB+D 120
The proposed method achieved state-of-the-art performance on NTU RGB+D dataset for 3D human action analysis.
Ranked #51 on Skeleton Based Action Recognition on NTU RGB+D
Although various methods have been proposed for 3D action recognition, some of which are basic and some use deep learning, the need of basic methods based on generalized eigenvalue problem is sensed for action recognition.