INTRODUCING
Reading Recognition in the Wild
100 hours of egocentric RGB video, eye gaze, and head pose data of reading and non-reading activities collected in diverse and realistic scenarios.
WHAT IS IT?
100 hours of reading recognition in-the-wild
A first-of-its-kind dataset, Reading Recognition in the Wild is designed to help solve the task of reading recognition by wearable devices in diverse environments. Reading Recognition in the Wild is a large-scale multimodal dataset comprising 100 hours of reading and non-reading videos captured in diverse and realistic scenarios using Aria Gen 1 glasses. The dataset features video, eye gaze, and head pose sensor outputs, created to help solve the task of reading recognition from a wearable device.
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Dataset Contents
- 100 hours of video data
- 1716 sequences
- 111 participants
- 60 Hz high-frequency eye tracking
- 19 high-level scenarios of reading and non-reading activities
- 150+ Reading materials
- Includes non-reading activities containing both daily activities and hard negatives (where text is present but is not being read).
Sensor Data
- 1 x 110 degree FOV + 30 Hz Rolling Shutter RGB camera
- 2 x 80 degree FOV + 60 Hz, Global Shutter mono cameras for eye-tracking with IR illumination
- 2 x 150 degree FOV + 30 Hz Global Shutter mono cameras for SLAM and hand tracking
- 1 kHz + 800 Hz IMUs + barometer & magnetometer environmental sensors
- 7 x 48 KHz spatial microphones
Annotations
- Start and end time of reading activities in each sequences
- Type of Reading material used
- Type of reading
- The participant’s description of the reading material
- 3D eye gaze
- 6DoF device trajectory
WHAT MAKES IT SPECIAL?
Real-world multimodal egocentric data for reading recognition in the wild
Diverse scenes and scenarios
- 19 high-level scenarios of reading and non-reading activities*
- Various indoor and outdoor locations: Offices, Libraries, Lounges, Homes, Stores, Balconies, Patios, Roads, Trails, In the Woods.
Various Reading Modes
Our dataset includes multiple reading modes including:
- Engaged reading
- Skimming
- Scanning
- Reading out loud
- Multi-tasking (While walking, Writing/typing)
Comprehensive Reading Materials
150+ Reading materials in different mediums and text lengths.
Annotations
The recordings were processed by Aria Machine Perception Service to obtain accurate 6 DoF device trajectory, semi-dense point clouds, and 3D eye gaze ray estimation with depth.
In addition, the annotation contains
- Start and end time of reading activities in each sequences
- Type of reading material used
- Type of reading mode
- The participant’s description of the reading material
Seattle and Columbus subsets
The Reading Recognition in the Wild dataset contains two distinct subsets, one captured in Columbus and the other captured in Seattle. The Seattle subset focuses on diversity, while the Columbus subset aims to test our model’s ability to generalize in unseen settings, as well as identify edge cases where the model fails.
Seattle subset
The Seattle subset emphasizes diversity, comprising 80 hours of data from 80 participants engaged in various reading and non-reading activities across multiple indoor and outdoor settings.
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Columbus subset
The Columbus subset is designed to evaluate edge cases, comprising approximately 20 hours of data from 31 subjects containing reading and non-reading activities in indoor settings. It also features 4 different languages.
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Read the Reading Recognition in the Wild Research Paper
For more information about our motivations, methods, and dataset details read our research paper on arXiv.
READ THE RESEARCH PAPER
Access Reading Recognition in the Wild Dataset
If you are a researcher interested in recreating our method of scene reconstruction, access the Aria Scene Reconstruction Dataset to get started.
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