Research Article
Instructor Activity Recognition through Deep Spatiotemporal Features and Feedforward Extreme Learning Machines
Table 5
Performance comparison of various variants of ELM using a deep spatiotemporal representation of action motion templates of IAVID-1 dataset.
| Method | IAVID (70-30 split) | MuHAVi-Uncut (LOCO) | Accuracy | Computational Time | Accuracy | Computational Time |
| ELM | 0.81250 | 6.05 m sec | 82.04% | 2280 sec | RELM [27] | 0.81250 | 6.29 m sec | 82.04% | 2340 sec | SADE ELM [28] | 0.235 | 0.9829 sec | 50.98% | 4080 sec | MCV-ELM [29] | 0.1225 | 33.17sec | 74.75% | 5280 sec | MV-ELM [30] | 0.1225 | 32.41 sec | 74.75% | 4380 sec |
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