DHP19: Dynamic Vision Sensor 3D Human Pose Dataset

• Published in: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) pp. 1695 - 1704
• Main Authors: Calabrese, Enrico, Taverni, Gemma, Easthope, Christopher Awai, Skriabine, Sophie, Corradi, Federico, Longinotti, Luca, Eng, Kynan, Delbruck, Tobi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2019
• Subjects: Cameras; Pose estimation; Solid modeling; Three-dimensional displays; Two dimensional displays; Vision sensors; Voltage control
• ISSN: 2160-7516
• DOI: 10.1109/CVPRW.2019.00217

Human pose estimation has dramatically improved thanks to the continuous developments in deep learning. However, marker-free human pose estimation based on standard frame-based cameras is still slow and power hungry for real-time feedback interaction because of the huge number of operations necessary for large Convolutional Neural Network (CNN) inference. Event-based cameras such as the Dynamic Vision Sensor (DVS) quickly output sparse moving-edge information. Their sparse and rapid output is ideal for driving low-latency CNNs, thus potentially allowing real-time interaction for human pose estimators. Although the application of CNNs to standard frame-based cameras for human pose estimation is well established, their application to event-based cameras is still under study. This paper proposes a novel benchmark dataset of human body movements, the Dynamic Vision Sensor Human Pose dataset (DHP19). It consists of recordings from 4 synchronized 346x260 pixel DVS cameras, for a set of 33 movements with 17 subjects. DHP19 also includes a 3D pose estimation model that achieves an average 3D pose estimation error of about 8 cm, despite the sparse and reduced input data from the DVS.