Published December 2, 2021
| Version
v17
Dataset
Open
TAD-Net: An Approach for Realtime Action Detection Based on TCN and GCN in Digital Twin Shop-floor
Creators
Description
We proposed a real-time detection approach for shop-floor production action, this approach took the sequence data of continuous human skeleton joints sequence as input, reconstructed the Joint Classification-Regression Recurrent Neural Networks (JCR-RNN) based on Temporal Convolution Network (TCN) and Graph Convolution Network (GCN), constructed our Temporal Action Detection Net (TAD-Net), realized real-time shop-floor production action detection.
DO I(10.12688/digitaltwin.17408.1)
Files
confuseMatrix1.csv
Files
(131.5 MB)
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PID: http://hdl.handle.net/11304/93caa805-7faf-4447-b363-116e46619e99 |
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Additional details
Identifiers
- B2SHARE Legacy Record ID
- 7a1fca332ca84f018cab2c795cea3d37
- B2SHARE Legacy Record ID
- e7c972851c1946b1ba1b6e37d0065795
InGRID metadata
- Access
- Freely accessible for non-commercial use
- Data providers
- Qing Hong
- Guidelines for use available
- True
- Indicators available
- True
- Search or browse function available online
- True
- Text documents available
- True