Published August 14, 2021
| Version
v4
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.
Files
confuseMatrix1.csv
Files
(108.7 MB)
| Name | Size | Download all |
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Checksum: md5:4afb207cbcb25747a6a498a5ceb98c6f
PID: http://hdl.handle.net/11304/35843cf5-a5d2-439f-86c1-d1848f06577a |
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Checksum: md5:6ae6b6edcb95dfc4e65bc83e52ad355e
PID: http://hdl.handle.net/11304/a04e7c0d-45a3-42ef-b0fb-c31810dbe82a |
108.7 MB | Download |
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Checksum: md5:44fdd80ed94cad255bdc276eea049c2e
PID: http://hdl.handle.net/11304/1f3e8a5d-036c-44c2-9a5a-75d1060d8ccb |
34.7 kB | Download |
Additional details
Identifiers
- B2SHARE Legacy Record ID
- fe26953eca894b3191992b347a07cfa9
- B2SHARE Legacy Record ID
- b7a5c5cc2a064a63b6609f2098dfc386
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