scholarly journals Action Detection by Implicit Intentional Motion Clustering

2015 ◽  
Author(s):  
Wei Chen ◽  
Jason J. Corso
Keyword(s):  
Action Detection ◽  
Intentional Motion ◽  
Motion Clustering

Human Action Detection Based on Tracking Region of Maximum Mutual Information

2012 ◽  
Vol 38 (12) ◽  
pp. 2023 ◽  
Author(s):  
Tai-Qing WANG ◽  
Sheng-Jin WANG ◽  
Xiao-Qing DING
Keyword(s):  
Mutual Information ◽  
Human Action ◽  
Action Detection ◽  
Human Action Detection ◽  
Maximum Mutual Information

Graph-based motion prediction for abnormal action detection

2021 ◽  
Author(s):  
Yao Tang ◽  
Lin Zhao ◽  
Zhaoliang Yao ◽  
Chen Gong ◽  
Jian Yang
Keyword(s):  
Motion Prediction ◽  
Action Detection

scholarly journals Video action detection by learning graph-based spatio-temporal interactions

2021 ◽  
Vol 206 ◽  
pp. 103187
Author(s):  
Matteo Tomei ◽  
Lorenzo Baraldi ◽  
Simone Calderara ◽  
Simone Bronzin ◽  
Rita Cucchiara
Keyword(s):  
Action Detection ◽  
Spatio Temporal ◽  
Temporal Interactions

scholarly journals Skeleton‐based attention‐aware spatial–temporal model for action detection and recognition

2020 ◽  
Vol 14 (5) ◽  
pp. 177-184
Author(s):  
Ran Cui ◽  
Aichun Zhu ◽  
Jingran Wu ◽  
Gang Hua
Keyword(s):  
Action Detection ◽  
Temporal Model ◽  
Detection And Recognition

scholarly journals Learning Spatial and Temporal Extents of Human Actions for Action Detection

2015 ◽  
Vol 17 (4) ◽  
pp. 512-525 ◽  
Author(s):  
Zhong Zhou ◽  
Feng Shi ◽  
Wei Wu
Keyword(s):  
Human Actions ◽  
Action Detection

Discriminative subvolume search for efficient action detection

2009 ◽  
Author(s):  
Junsong Yuan ◽  
Zicheng Liu ◽  
Ying Wu
Keyword(s):  
Action Detection

TAD-Net: An approach for real-time action detection based on temporal convolution network and graph convolution network in digital twin shop-floor

Digital Twin ◽  
2021 ◽  
Vol 1 ◽  
pp. 10
Author(s):  
Qing Hong ◽  
Yifeng Sun ◽  
Tingyu Liu ◽  
Liang Fu ◽  
Yunfeng Xie
Keyword(s):  
Real Time ◽  
Sequence Data ◽  
Network Models ◽  
Human Action ◽  
Practical Significance ◽  
Shop Floor ◽  
Action Detection ◽  
Digital Twin ◽  
Actual Application ◽  
Production Processes

Background: Intelligent monitoring of human action in production is an important step to help standardize production processes and construct a digital twin shop-floor rapidly. Human action has a significant impact on the production safety and efficiency of a shop-floor, however, because of the high individual initiative of humans, it is difficult to realize real-time action detection in a digital twin shop-floor. Methods: We proposed a real-time detection approach for shop-floor production action. This approach used the sequence data of continuous human skeleton joints sequences as the input. We then reconstructed the Joint Classification-Regression Recurrent Neural Networks (JCR-RNN) based on Temporal Convolution Network (TCN) and Graph Convolution Network (GCN). We called this approach the Temporal Action Detection Net (TAD-Net), which realized real-time shop-floor production action detection. Results: The results of the verification experiment showed that our approach has achieved a high temporal positioning score, recognition speed, and accuracy when applied to the existing Online Action Detection (OAD) dataset and the Nanjing University of Science and Technology 3 Dimensions (NJUST3D) dataset. TAD-Net can meet the actual needs of the digital twin shop-floor. Conclusions: Our method has higher recognition accuracy, temporal positioning accuracy, and faster running speed than other mainstream network models, it can better meet actual application requirements, and has important research value and practical significance for standardizing shop-floor production processes, reducing production security risks, and contributing to the understanding of real-time production action.

Capsule Boundary Network with 3D Convolutional Dynamic Routing for Temporal Action Detection

2021 ◽  
pp. 1-1
Author(s):  
Yaosen Chen ◽  
Bing Guo ◽  
Yan Shen ◽  
Wei Wang ◽  
Weichen Lu ◽  
...  
Keyword(s):  
Dynamic Routing ◽  
Action Detection ◽  
Temporal Action
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