Published August 5, 2021
| Version
v1
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
(543.6 MB)
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Checksum: md5:4afb207cbcb25747a6a498a5ceb98c6f
PID: http://hdl.handle.net/11304/57c8410d-5845-4698-bf9c-4db350052c63 |
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Checksum: md5:44fdd80ed94cad255bdc276eea049c2e
PID: http://hdl.handle.net/11304/67a75468-7dc6-4952-a38b-4db4ca76b844 |
34.7 kB | Download |
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Checksum: md5:fa4e41736b1cb3b2d7b498aa28513829
PID: http://hdl.handle.net/11304/8cf830db-aa93-447f-8178-842dc7c7d2e7 |
543.6 MB | Download |
Additional details
Identifiers
- B2SHARE Legacy Record ID
- cce7783d5ffa4c1ab1e0bceb35f62b50
- B2SHARE Legacy Record ID
- 1de9c6e485344ccaa4ff1e6c47c6196b
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