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Human Body Orientation Estimation using Convolutional Neural Network

Authors :
Choi, Jinyoung
Lee, Beom-Jin
Zhang, Byoung-Tak
Publication Year :
2016

Abstract

Personal robots are expected to interact with the user by recognizing the user's face. However, in most of the service robot applications, the user needs to move himself/herself to allow the robot to see him/her face to face. To overcome such limitations, a method for estimating human body orientation is required. Previous studies used various components such as feature extractors and classification models to classify the orientation which resulted in low performance. For a more robust and accurate approach, we propose the light weight convolutional neural networks, an end to end system, for estimating human body orientation. Our body orientation estimation model achieved 81.58% and 94% accuracy with the benchmark dataset and our own dataset respectively. The proposed method can be used in a wide range of service robot applications which depend on the ability to estimate human body orientation. To show its usefulness in service robot applications, we designed a simple robot application which allows the robot to move towards the user's frontal plane. With this, we demonstrated an improved face detection rate.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.1609.01984
Document Type :
Working Paper