Space-time representation of people based on 3D skeletal data
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TLDR
Skeleton-based human representations have been intensively studied and kept attracting an increasing attention, due to their robustness to variations of viewpoint, human body scale and motion speed as well as the real-time, online performance as mentioned in this paper.About:
This article is published in Computer Vision and Image Understanding.The article was published on 2017-05-01 and is currently open access. It has received 279 citations till now.read more
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NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
TL;DR: In this paper, a large-scale dataset for RGB+D human action recognition was introduced with more than 56 thousand video samples and 4 million frames, collected from 40 distinct subjects.
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Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
TL;DR: This paper introduces new gating mechanism within LSTM to learn the reliability of the sequential input data and accordingly adjust its effect on updating the long-term context information stored in the memory cell, and proposes a more powerful tree-structure based traversal method.
Journal ArticleDOI
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
TL;DR: This work introduces a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames, and investigates a novel one-shot 3D activity recognition problem on this dataset.
Proceedings Article
An end-to-end spatio-temporal attention model for human action recognition from skeleton data
TL;DR: Zhang et al. as mentioned in this paper proposed an end-to-end spatial and temporal attention model for human action recognition from skeleton data, which learns to selectively focus on discriminative joints of skeleton within each frame of the inputs and pays different levels of attention to the outputs of different frames.
Proceedings ArticleDOI
Global Context-Aware Attention LSTM Networks for 3D Action Recognition
TL;DR: This work proposes a new class of LSTM network, Global Context-Aware Attention L STM (GCA-LSTM), for 3D action recognition, which is able to selectively focus on the informative joints in the action sequence with the assistance of global contextual information.
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