Adversarial action data augmentation for similar gesture action recognition

Paper

Wu, Di, Chen, Junjun, Sharma, Nabin, Pan, Shirui, Long, Guodong and Blumenstein, Michael. 2019. "Adversarial action data augmentation for similar gesture action recognition." 2019 International Joint Conference on Neural Networks (IJCNN). Budapest, Hungary 14 - 19 Jul 2019 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN.2019.8851993
Paper/Presentation Title

Adversarial action data augmentation for similar gesture action recognition

Presentation TypePaper
AuthorsWu, Di, Chen, Junjun, Sharma, Nabin, Pan, Shirui, Long, Guodong and Blumenstein, Michael
Journal or Proceedings TitleProceedings of 2019 International Joint Conference on Neural Networks (IJCNN)
Number of Pages8
Year2019
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
Digital Object Identifier (DOI)https://doi.org/10.1109/IJCNN.2019.8851993
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/abstract/document/8851993
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/8840768/proceeding
Conference/Event2019 International Joint Conference on Neural Networks (IJCNN)
Event Details
2019 International Joint Conference on Neural Networks (IJCNN)
Parent
International Joint Conference on Neural Networks (IJCNN)
Delivery
In person
Event Date
14 to end of 19 Jul 2019
Event Location
Budapest, Hungary
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.

KeywordsSimilar gestures; Action recognition; Neural Networks; Deep learning
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
460304. Computer vision
Public Notes

Files associated with this item cannot be displayed due to copyright restrictions, but may be accessed online. Please see the link in the URL field.

Byline AffiliationsUniversity of Technology Sydney
Beijing University of Chemical Technology, China
Monash University
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