Abstract
Human gestures are unique for recognizing and describing human actions, and video-based human action recognition techniques are effective solutions to varies real-world applications, such as surveillance, video indexing, and human-computer interaction. Most existing video human action recognition approaches either using handcraft features from the frames or deep learning models such as convolutional neural networks (CNN) and recurrent neural networks (RNN); however, they have mostly overlooked the similar gestures between different actions when processing the frames into the models. The classifiers suffer from similar features extracted from similar gestures, which are unable to classify the actions in the video streams. In this paper, we propose a novel framework with generative adversarial networks (GAN) to generate the data augmentation for similar gesture action recognition. The contribution of our work is tri-fold: 1) we proposed a novel action data augmentation framework (ADAF) to enlarge the differences between the actions with very similar gestures; 2) the framework can boost the classification performance either on similar gesture action pairs or the whole dataset; 3) experiments conducted on both KTH and UCF101 datasets show that our data augmentation framework boost the performance on both similar gestures actions as well as the whole dataset compared with baseline methods such as 2DCNN and 3DCNN.
Original language | English |
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Title of host publication | International Joint Conference on Neural Networks (IJCNN) 2019 |
Editors | Plamen Angelov, Manuel Roveri |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Number of pages | 8 |
ISBN (Electronic) | 9781728119854 |
ISBN (Print) | 9781728119861 |
DOIs | |
Publication status | Published - 2019 |
Event | IEEE International Joint Conference on Neural Networks 2019 - Budapest, Hungary Duration: 14 Jul 2019 → 19 Jul 2019 https://ieeexplore.ieee.org/xpl/conhome/8840768/proceeding (Proceedings) |
Conference
Conference | IEEE International Joint Conference on Neural Networks 2019 |
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Abbreviated title | IJCNN 2019 |
Country/Territory | Hungary |
City | Budapest |
Period | 14/07/19 → 19/07/19 |
Internet address |
Keywords
- Action recognition
- Deep learning
- Neural Networks
- Similar gestures