Connectionist temporal classification model for dynamic hand gesture recognition using RGB and optical flow data
Joint Authors
Source
The International Arab Journal of Information Technology
Issue
Vol. 17, Issue 4 (31 Jul. 2020), pp.497-506, 10 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2020-07-31
Country of Publication
Jordan
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger.
Due to a number of challenges many researchers focus on this area.
Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification.
In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation.
We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video.
CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream.
CTC network finds the most probable sequence of a frame for a class of gesture.
The frame with the highest probability value is selected from the CTC network by max decoding.
This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture.
We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data.
On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%.
American Psychological Association (APA)
Patel, Sunil& Makwana, Ramji. 2020. Connectionist temporal classification model for dynamic hand gesture recognition using RGB and optical flow data. The International Arab Journal of Information Technology،Vol. 17, no. 4, pp.497-506.
https://search.emarefa.net/detail/BIM-1430884
Modern Language Association (MLA)
Patel, Sunil& Makwana, Ramji. Connectionist temporal classification model for dynamic hand gesture recognition using RGB and optical flow data. The International Arab Journal of Information Technology Vol. 17, no. 4 (Jul. 2020), pp.497-506.
https://search.emarefa.net/detail/BIM-1430884
American Medical Association (AMA)
Patel, Sunil& Makwana, Ramji. Connectionist temporal classification model for dynamic hand gesture recognition using RGB and optical flow data. The International Arab Journal of Information Technology. 2020. Vol. 17, no. 4, pp.497-506.
https://search.emarefa.net/detail/BIM-1430884
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 504-505
Record ID
BIM-1430884