Head Pose Detection for a Wearable Parrot-Inspired Robot Based on Deep Learning
Source abstract
Abstract
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’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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Face recognition and analysis · Human-Animal Interaction Studies · Social Robot Interaction and HRI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Biomedical Technologies, Auckland University of Technology, Auckland 1010, New Zealand
Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore 487372, Singapore
School of Public Health and Psychosocial Studies, Auckland University of Technology, Auckland 1010, New Zealand
Funding
Auckland University of Technology, New Zealand
References (40)
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