Human Body Orientation Estimation using Convolutional Neural Network
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Face DetectionSimilar Papers 제목 키워드 기반
MEBOW: Monocular Estimation of Body Orientation In the Wild
Body orientation estimation provides crucial visual cues in many applications, including robotics and autonomous driving. It is particularly desirable when 3-D pose estimation is difficult to infer due to poor image reso…
Autonomous DrivingPose EstimationPedRecNet: Multi-task deep neural network for full 3D human pose and orientation estimation
We present a multitask network that supports various deep neural network based pedestrian detection functions. Besides 2D and 3D human pose, it also supports body and head orientation estimation based on full body boundi…
3D Human Pose EstimationFace RecognitionPedestrian DetectionPose EstimationRadar-Based Estimation of Human Body Orientation Using Respiratory Features and Hierarchical Regression Model
This study proposes an accurate method to estimate human body orientation using a millimeter-wave radar system. Body displacement is measured from the phase of the radar echo, which is analyzed to obtain features associa…
regressionBody and Head Orientation Estimation from Low-Resolution Point Clouds in Surveillance Settings
We propose a system that estimates people's body and head orientations using low-resolution point cloud data from two LiDAR sensors. Our models make accurate estimations in real-world conversation settings where subjects…
Orientation Driven Bag of Appearances for Person Re-identification
Person re-identification (re-id) consists of associating individual across camera network, which is valuable for intelligent video surveillance and has drawn wide attention. Although person re-identification research is …
Person Re-Identification