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About Rollback Based Active Semi-Super Vised Learning For Action Recognion - Pageindexforupload
This doctoral thesis studies roll back based active semi-supervised learning for human action recognition. The thesis presents a method using an ensemble of multiple deep convolutional neural network models for training data to recognize human actions. Labeling large volumes of video frame data for human action is expensive and time consuming, so the proposed method combines active learning with semi-supervised learning to reduce labeling needs. Experiments show the approach achieves 95.6% accuracy on simple environment data and 81% accuracy on complex data, outperforming other state-of-the-art methods. The thesis explores recognizing single person actions and two-person interactions.
Detailed Information
| Author: | ['minhaz21'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 101 |
| Language: | English |
| Format: | |
| Price: | FREE |
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