Published August 14, 2021
| Version
v5
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/11304/3831026c-c9f6-4283-9b0a-bbfad0f5a5ae |
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Checksum: md5:db601b78791cf447aebfea544a666fcd
PID: http://hdl.handle.net/11304/c4cbca5f-d3a2-498a-b55e-98bff6960544 |
108.7 MB | Download |
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Checksum: md5:44fdd80ed94cad255bdc276eea049c2e
PID: http://hdl.handle.net/11304/777043e7-60d4-4853-b98b-02ff1409eef2 |
34.7 kB | Download |
Additional details
Identifiers
- B2SHARE Legacy Record ID
- c4948bc2b59d4f309977e62fbdb2d264
- B2SHARE Legacy Record ID
- 0023b2f1b6844c83a5ab5049d1e6c6d3
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