Head Pose Detection for a Wearable Parrot-Inspired Robot Based on Deep Learning
| Autor: | Chris Krägeloh, R Elara Mohan, Thejus Pathmakumar, Ahmed M. Al-Jumaily, Loulin Huang, Jaishankar Bharatharaj |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Head (linguistics) Computer science Wearable computer 02 engineering and technology lcsh:Technology Convolutional neural network lcsh:Chemistry 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering General Materials Science Computer vision lcsh:QH301-705.5 Instrumentation parrot-inspired robot Fluid Flow and Transfer Processes Human head lcsh:T business.industry Process Chemistry and Technology Deep learning General Engineering technology industry and agriculture deep learning head pose detection lcsh:QC1-999 wearable pet robot Computer Science Applications lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 bio inspired robot Robot 020201 artificial intelligence & image processing Artificial intelligence lcsh:Engineering (General). Civil engineering (General) business lcsh:Physics |
| Zdroj: | Applied Sciences Volume 8 Issue 7 Applied Sciences, Vol 8, Iss 7, p 1081 (2018) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app8071081 |
| Popis: | Extensive research has been conducted in human head pose detection systems and several applications have been identified to deploy such systems. Deep learning based head pose detection is one such method which has been studied for several decades and reports high success rates during implementation. Across several pet robots designed and developed for various needs, there is a complete absence of wearable pet robots and head pose detection models in wearable pet robots. Designing a wearable pet robot capable of head pose detection can provide more opportunities for research and development of such systems. In this paper, we present a novel head pose detection system for a wearable parrot-inspired pet robot using images taken from the wearer&rsquo s shoulder. This is the first time head pose detection has been studied in wearable robots and using images from a side angle. In this study, we used AlexNet convolutional neural network architecture trained on the images from the database for the head pose detection system. The system was tested with 250 images and resulted in an accuracy of 94.4% across five head poses, namely left, left intermediate, straight, right, and right intermediate. |
| Databáze: | OpenAIRE |
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