| --- |
| license: cc-by-4.0 |
| task_categories: |
| - other |
| tags: |
| - eye-tracking |
| - saliency |
| - visual-attention |
| - gaze |
| - fixations |
| - polygonal-aperture |
| pretty_name: "PolyCAT: Polygon-Aperture Eye-Tracking Dataset" |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # PolyCAT: Polygon-Aperture Eye-Tracking Dataset |
|
|
| PolyCAT is a public eye-tracking dataset for studying visual attention on natural images viewed through irregular polygon apertures. The dataset is designed to support saliency prediction, gaze modeling, and research on how geometric viewing constraints affect visual exploration strategies. |
|
|
| ## Recording Details |
|
|
| - **Eye tracker:** EyeLink 1000+ (SR Research), head-mounted, binocular, 500 Hz per eye |
| - **Display:** 27" 4K monitor (3840 x 2160 pixels) at 144 Hz |
| - **Viewing distance:** 70 cm |
| - **Participants:** 30 included (ages 24-29, 14 female / 16 male) |
| - **Stimuli:** 600 images from CAT2000 (6 categories) viewed through 27 polygon aperture shapes |
| - **Trials per participant:** 702 (351 per part x 2 parts) |
| - **Stimulus duration:** 4.0 seconds per trial |
| - **Secondary task:** Old/new memory probe after each block |
|
|
| ## Folder Overview |
|
|
| ``` |
| PolyCAT/ |
| ├── data/ |
| │ ├── metadata/ # Consolidated tables: participants, sessions, trials, quality |
| │ ├── gaze/ # Sample-level gaze streams (TSV, one per session) |
| │ ├── fixations/ # Fixation events (CSV, global + per-participant) |
| │ ├── saliency_maps/ # Empirical fixation density maps per polygon-image |
| │ ├── scanpaths/ # Per-trial fixation sequences |
| │ └── stimuli/ # CAT2000 images and polygon definitions (JSON) |
| ├── code/ |
| │ └── examples/ # Quickstart scripts and notebooks |
| ├── manifests/ # Stimulus manifest, polygon geometry |
| └── docs/ # Data dictionary, file formats, acquisition protocol |
| ``` |
|
|
| ## Getting Started |
|
|
| ```python |
| import pandas as pd |
| |
| # Load metadata |
| participants = pd.read_csv("data/metadata/participants.csv") |
| trials = pd.read_csv("data/metadata/trials.csv") |
| fixations = pd.read_csv("data/fixations/fixations_all.csv") |
| |
| # Filter to one participant |
| p01_fix = fixations[fixations["participant_id"] == "P01"] |
| print(f"P01 has {len(p01_fix)} fixations across {p01_fix['trial_uid'].nunique()} trials") |
| |
| # Fixation heatmap for a specific polygon-image combination |
| import numpy as np |
| polygon = "polygon_25" |
| subset = fixations[(fixations["polygon_id"] == polygon) & (fixations["eye"] == "R")] |
| heatmap = np.zeros((2160, 3840)) |
| for _, row in subset.iterrows(): |
| x, y = int(row["x_px"]), int(row["y_px"]) |
| if 0 <= x < 3840 and 0 <= y < 2160: |
| heatmap[y, x] += 1 |
| ``` |
|
|
| ## Polygon Apertures |
|
|
| The 27 polygon shapes are organized into three groups: |
| - **Reference** (3): regular geometric shapes (rectangle, symmetric, asymmetric) |
| - **Convexity-varied** (3): shapes with varying convexity (convex, concave, intermediate) |
| - **Irregular** (21): asymmetric polygons with diverse geometric properties |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ```bibtex |
| @inproceedings{polycat2026, |
| title = {How Do We Look at Images Through Polygonal Apertures: The PolyCAT Dataset}, |
| author = {Aklilu, Brahan and Ben-Shahar, Ohad}, |
| booktitle = {Proceedings of the 2026 ACM Symposium on Eye Tracking Research \& Applications (ETRA '26)}, |
| year = {2026}, |
| doi = {TBD} |
| } |
| ``` |
|
|
| ## License |
|
|
| This dataset is released under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](LICENSE). |
|
|
