Similar Gesture Recognition using Hierarchical Classification Approach in RGB Videos

Paper

Wu, Di, Sharma, Nabin and Blumenstein, Michael. 2019. "Similar Gesture Recognition using Hierarchical Classification Approach in RGB Videos." 2018 Digital Image Computing: Techniques and Applications (DICTA). Canberra, Australia 10 - 13 Dec 2018 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/DICTA.2018.8615804

Paper/Presentation Title	Similar Gesture Recognition using Hierarchical Classification Approach in RGB Videos
Presentation Type	Paper
Authors	Wu, Di, Sharma, Nabin and Blumenstein, Michael
Journal or Proceedings Title	Proceedings of 2018 Digital Image Computing: Techniques and Applications (DICTA)
Number of Pages	7
Year	2019
Publisher	IEEE (Institute of Electrical and Electronics Engineers)
Place of Publication	United States
ISBN	9781538666029
Digital Object Identifier (DOI)	https://doi.org/10.1109/DICTA.2018.8615804
Web Address (URL) of Paper	https://ieeexplore.ieee.org/abstract/document/8615804
Web Address (URL) of Conference Proceedings	https://ieeexplore.ieee.org/xpl/conhome/8615628/proceeding
Conference/Event	2018 Digital Image Computing: Techniques and Applications (DICTA)
Event Details	2018 Digital Image Computing: Techniques and Applications (DICTA) Parent International Conference on Digital Image Computing: Techniques and Applications Delivery In person Event Date 10 to end of 13 Dec 2018 Event Location Canberra, Australia Rank B B B B B
Abstract	Recognizing human actions from the video streams has become one of the very popular research areas in computer vision and deep learning in the recent years. Action recognition is wildly used in different scenarios in real life, such as surveillance, robotics, healthcare, video indexing and human-computer interaction. The challenges and complexity involved in developing a video-based human action recognition system are manifold. In particular, recognizing actions with similar gestures and describing complex actions is a very challenging problem. To address these issues, we study the problem of classifying human actions using Convolutional Neural Networks (CNN) and develop a hierarchical 3DCNN architecture for similar gesture recognition. The proposed model firstly combines similar gesture pairs into one class, and classify them along with all other class, as a stage-1 classification. In stage-2, similar gesture pairs are classified individually, which reduces the problem to binary classification. We apply and evaluate the developed models to recognize the similar human actions on the HMDB51 dataset. The result shows that the proposed model can achieve high performance in comparison to the state-of-the-art methods.
Keywords	Action Recognition; Neural Networks; Deep Learning; Computer Vision
Contains Sensitive Content	Does not contain sensitive content
ANZSRC Field of Research 2020	4602. 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 Affiliations	University of Technology Sydney

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