Authors:
Yemineni Ashok
;
Mukesh Rohil
;
Kshitij Tandon
and
Harshil Sethi
Affiliation:
Birla Institute of Technology And Science, Pilani (BITS Pilani), Pilani, Rajasthan, India
Keyword(s):
Visual Odometry, Wearable Computing, Augmented Reality, Mixed Reality, Pose Estimation, Simultaneous Localization and Mapping.
Abstract:
Visual Odometry/ Simultaneous Localization and Mapping (VO/ SLAM) and Egocentric hand gesture recognition are the two major technologies for wearable computing devices like AR (Augmented Reality)/ MR
(Mixed Reality) glasses. However, the AR/MR community lacks a suitable dataset for developing both hand
gesture recognition and RGB-D SLAM methods. In this work, we use a ZED mini Camera to develop challenging benchmarks for RGB-D VO/ SLAM tasks and dynamic hand gesture recognition. In our dataset
VOEDHgesture, we collected 264 sequences using a ZED mini camera, along with precisely measured and
time-synchronized ground truth camera positions, and manually annotated the bounding box values for the
hand region of interest. The sequences comprise both RGB and depth images, captured at HD resolution
(1920 × 1080) and recorded at a video frame rate of 30Hz. To resemble the Augmented Reality environment, the sequences are captured using a head-mounted ZED mini camera, with unrestricted
6-DOF (degree
of freedom) movements in different varieties of scenes and camera motions, i.e. indoor, outdoor, slow motion,
quick motions, long trajectories, loop closures etc. This dataset can help researchers to develop and promote
reproducible research in the fields of egocentric hand tracking, visual odometry/SLAM and computer vision
algorithms for AR scene reconstruction and scene understanding, etc.
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