An egocentric video and eye-tracking dataset for visual search in convenience stores
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We introduce an egocentric video and eye-tracking dataset, comprised of 108 first-person videos of 36 shoppers searching for three different products (orange juice, KitKat chocolate bars, and canned tuna) in a convenience store, along with the frame-centered eye fixation locations for each video frame. The dataset also includes demographic information about each participant in the form of an 11-question survey. The paper describes two applications using the dataset — an analysis of eye fixations during search in the store, and a training of a clustered saliency model for predicting saliency of viewers engaged in product search in the store. The fixation analysis shows that fixation duration statistics are very similar to those found in image and video viewing, suggesting that similar visual processing is employed during search in 3D environments and during viewing of imagery on computer screens. A clustering technique was applied to the questionnaire data, which resulted in two clusters being detected. Based on these clusters, personalized saliency prediction models were trained on the store fixation data, which provided improved performance in prediction saliency on the store video data compared to state-of-the art universal saliency prediction methods. • Egocentric video dataset of shopper search in a convenience store. • Eye-tracking dataset of shopper search in a convenience store. • Analysis of fixated objects and fixation durations during search. • Fine-tuning of clustered task-dependent saliency models for search.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it