# Human Body Orientation Estimation using Convolutional Neural Network

arXiv: https://arxiv.org/abs/1609.01984 · HTML: https://mlsanity.com/paper/1609.01984/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2016.

## Built on by (most cited first, 1 of 1)

- [Joint Multi-Person Body Detection and Orientation Estimation via One Unified Embedding](https://mlsanity.com/paper/2210.15586/) (2022)

## Abstract (arXiv)

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.