Human Body Orientation Estimation using Convolutional Neural Network

TL;DR

This paper introduces a lightweight convolutional neural network for estimating human body orientation, enhancing robot interaction capabilities by enabling robots to better understand user positioning.

Contribution

The paper presents a novel end-to-end CNN model for human body orientation estimation, achieving higher accuracy than previous methods and demonstrating practical robot application benefits.

Findings

Achieved 81.58% accuracy on benchmark dataset

Achieved 94% accuracy on own dataset

Improved face detection rate in robot application

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.