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.

提出一种基于轻量级卷积神经网络的人体方向估计方法，用于改善面向服务型机器人应用中用户与机器人面对面交互时的限制，该方法在基准数据集和自行构建的数据集中分别达到了 81.58% 和 94% 的准确率，并可用于多种对估计人体方向敏感的面向服务型机器人应用，同时欲演示该方法对面部检测率的提升。