Over 200 Recorded Hours of Integrated Eye Movement, Odometry, and Egocentric Video in the Visual Experience Dataset

Keskeiset käsitteet

The Visual Experience Dataset provides over 240 hours of egocentric video combined with gaze and head tracking data, offering an unprecedented view of the visual world as experienced by human observers in naturalistic settings.

Tiivistelmä

The Visual Experience Dataset (VEDB) is a large-scale dataset that provides over 240 hours of egocentric video combined with gaze and head tracking data. The dataset was collected by 58 observers ranging from 6 to 49 years old, capturing a diverse range of everyday activities and environments.

Key highlights:

• The dataset consists of 717 sessions recorded across 124 different locations, representing 12 of the 16 top-level categories from the American Time Use Survey.
• Sessions include first-person egocentric video, binocular eye tracking, and head/body movement tracking, enabling research on natural scene statistics, gaze behavior, and head-eye coordination.
• The data collection process involved iterative design of custom headset mounts to accommodate different head sizes and face shapes, as well as challenges in eye tracking calibration and validation.
• Potential sources of error and bias, such as omissions, video recording issues, and eye tracking errors, are documented, and measures taken to mitigate privacy concerns are described.
• The dataset has a wide range of applications, including improving gaze tracking methodologies, assessing spatiotemporal image statistics, and refining deep neural networks for scene and activity recognition.
• The VEDB is publicly available through open science platforms, with plans for ongoing maintenance and community contributions to expand the dataset.

Tilastot

Over 240 hours of egocentric video data across 717 sessions
58 unique observers ranging from 6 to 49 years old
124 different locations represented, covering 12 of the 16 top-level categories from the American Time Use Survey
76% of sessions had successful gaze calibration, with 64% having validated gaze error under 5 degrees of visual angle

Lainaukset

"The VEDB's potential applications are vast, including improving gaze tracking methodologies, assessing spatiotemporal image statistics, and refining deep neural networks for scene and activity recognition."
"By providing a dataset grounded in real-world experiences and accompanied by extensive metadata and supporting code, the authors invite the research community to utilize and contribute to the VEDB, facilitating a richer understanding of visual perception and behavior in naturalistic settings."

Tärkeimmät oivallukset

The Visual Experience Dataset: Over 200 Recorded Hours of Integrated Eye Movement, Odometry, and Egocentric Video

by Michelle R. ... klo arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.18934.pdf

Syvällisempiä Kysymyksiä

How can the VEDB dataset be used to develop more robust and generalizable computer vision models for real-world applications?

The Visual Experience Dataset (VEDB) offers a unique opportunity to enhance computer vision models for real-world applications by providing a comprehensive view of human visual experiences. Researchers can leverage the integrated eye movement, odometry, and egocentric video data to train and validate models that can better understand and interpret visual scenes.

Improving Gaze Tracking Methodologies: The dataset includes gaze and head-tracking data, allowing for the development and validation of more accurate and robust gaze tracking algorithms. By analyzing how humans naturally move their eyes and heads in different environments and tasks, researchers can refine gaze estimation models for various applications, such as human-computer interaction and virtual reality systems.

Assessing Spatiotemporal Image Statistics: The VEDB provides a rich source of data for studying spatiotemporal image statistics in natural scenes. By analyzing the visual experiences captured in the dataset, researchers can gain insights into the temporal dynamics of visual perception, object recognition, and scene understanding. This information can be used to improve computer vision models' ability to process and interpret dynamic visual information.

Refining Deep Neural Networks for Scene and Activity Recognition: The dataset's annotations of scene locations and observer tasks enable the training and evaluation of deep neural networks for scene and activity recognition. Researchers can use the labeled data to develop models that can accurately classify scenes and activities based on egocentric video data, leading to more robust and generalizable computer vision systems for real-world applications.

In summary, the VEDB dataset provides a valuable resource for advancing computer vision research by enabling the development of more robust and generalizable models through the analysis of integrated eye movement, odometry, and egocentric video data.

What are the potential biases and limitations in the current dataset, and how can future data collection efforts address these issues?

The current VEDB dataset, while comprehensive and valuable, may have potential biases and limitations that could impact the generalizability and applicability of research findings.

Sampling Bias: The dataset's composition may not fully represent the diversity of visual experiences across different demographics, environments, and tasks. Future data collection efforts should aim to include a more diverse range of participants, activities, and locations to mitigate sampling bias and ensure the dataset's broader applicability.

Data Quality and Errors: The dataset may contain errors or inaccuracies in eye tracking, odometry, or video recordings, which could introduce biases in the analysis. Future data collection efforts should focus on improving data quality assurance measures, such as rigorous calibration procedures, validation checks, and error correction protocols, to enhance the dataset's reliability and validity.

Privacy and Ethical Considerations: The dataset's release and use raise privacy concerns, especially regarding the identifiable information of participants and bystanders. Future data collection efforts should prioritize participant privacy and data anonymization to protect individuals' identities and ensure ethical compliance in research practices.

Task and Environment Representation: The dataset's coverage of specific tasks and environments may be limited, leading to biases in the analysis and model generalizability. Future data collection efforts should aim to capture a more diverse and representative range of activities, locations, and scenarios to address these limitations and enhance the dataset's richness and diversity.

By addressing these potential biases and limitations through improved sampling strategies, data quality assurance measures, privacy protection protocols, and task/environment representation, future data collection efforts can enhance the VEDB dataset's quality, reliability, and applicability for a wide range of computer vision research applications.

How can the insights gained from analyzing the natural statistics of visual experience be applied to improve human-computer interaction and augmented/virtual reality systems?

Analyzing the natural statistics of visual experience, as captured in the VEDB dataset, can offer valuable insights that can be applied to enhance human-computer interaction (HCI) and augmented/virtual reality (AR/VR) systems in the following ways:

Enhanced User Experience: By understanding how humans naturally perceive and interact with visual stimuli in real-world environments, HCI systems can be designed to better align with users' cognitive processes and visual behaviors. Insights from natural scene statistics can inform the design of user interfaces, visualizations, and interaction techniques that are more intuitive and user-friendly.

Personalized Content Delivery: Analyzing the statistics of gaze behavior and visual attention can enable AR/VR systems to deliver personalized content based on users' interests, preferences, and visual focus. By tracking users' eye movements and understanding their visual preferences, systems can adapt content presentation, navigation, and interaction to enhance user engagement and satisfaction.

Optimized Immersive Experiences: Understanding the spatiotemporal dynamics of visual perception can help optimize AR/VR environments for more immersive and realistic experiences. By incorporating natural scene statistics into the design of virtual environments, developers can create more visually compelling and engaging simulations that closely mimic real-world visual experiences.

Efficient Interaction Design: Insights from analyzing head and eye movements in naturalistic settings can inform the design of more efficient and effective interaction techniques in HCI and AR/VR systems. By studying how users naturally move their heads and eyes during tasks and activities, designers can develop interaction methods that are ergonomic, intuitive, and responsive to users' needs.

In conclusion, leveraging the insights gained from analyzing the natural statistics of visual experience can lead to significant advancements in HCI and AR/VR systems, enabling the development of more user-centric, personalized, and immersive technologies that enhance user engagement and interaction in virtual environments.