Add paper link and update task categories

README.md

@@ -1,25 +1,29 @@
 ---
-viewer: false
 license: cc-by-4.0
+size_categories:
+- 1K<n<10K
 task_categories:
-  - object-detection
+- object-detection
+- robotics
+- image-segmentation
+viewer: false
 tags:
-  - aerial-ground
-  - multi-robot
-  - person-detection
-  - occlusion
-  - ros2
-  - outdoor
-  - synchronized
-size_categories:
-  - 1K<n<10K
+- aerial-ground
+- multi-robot
+- person-detection
+- occlusion
+- ros2
+- outdoor
+- synchronized
 ---
 
 # Co-GLANCE Dataset
 
+[**Project Page**](https://co-glance.github.io/)
+
 ## Dataset Summary
 
-[Co-GLANCE](co-glance.github.io) is a real-world aerial–ground synchronized dataset for person detection and multi-robot perception research. It provides over 4,000 synchronized RGB frames from aerial and ground viewpoints across two outdoor collection events, recorded on semi-structured terrain. Unlike simulation-based or road-scene datasets, Co-GLANCE offers raw sensor streams from heterogeneous robot platforms alongside ground-truth bounding box annotations, making it suitable for evaluating perception stacks under realistic conditions including occlusion, camouflage, and multi-person scenes.
+[Co-GLANCE](https://co-glance.github.io) is a real-world aerial–ground synchronized dataset for person detection and multi-robot perception research. It provides over 4,000 synchronized RGB frames from aerial and ground viewpoints across two outdoor collection events, recorded on semi-structured terrain. Unlike simulation-based or road-scene datasets, Co-GLANCE offers raw sensor streams from heterogeneous robot platforms alongside ground-truth bounding box annotations, making it suitable for evaluating perception stacks under realistic conditions including occlusion, camouflage, and multi-person scenes.
 
 Raw ROS 2 bag files from both platforms are also released separately to support broader evaluation of perception and autonomy stacks beyond static image benchmarks.
 
@@ -27,7 +31,7 @@ Raw ROS 2 bag files from both platforms are also released separately to support
 
 The dataset is organized into two collection scenarios:
 
-| | Construction Scenario (2025-03-30) | Camouflage Scenario  (2025-04-14) |
+| | Construction Scenario (2026-03-30) | Camouflage Scenario  (2026-04-14) |
 |---|---|---|
 | **Scenario** | A construction worker walks through a construction site, followed by a ground robot and an aerial robot recording the scene. | Two individuals wearing camouflage attempt to move through a visually occluded area. |
 | **Ground Hardware** | GoPro HERO 10 | Boston Dynamics Spot — front-left and front-right cameras (stitched) |
@@ -44,11 +48,11 @@ Each run is split into scene-type categories depending on the event:
 
 | Category | Construction Scenario (03-30) | Camouflage Scenario (04-14) |
 |---|---|---|
-| **Clean** | Scenes with exactly one person clearly visible. Represents the primary target for single-person detection and tracking. | Scenes where both individuals are fully visible with no occlusion. |
+| **Clean** | Scenes with exactly one person clearly visible. Represents the primary target for single-person detection and tracking. | Scenes where both individuals are at least partially visible. |
 | **Filtered Out** | Scenes with no humans present. Excluded from primary detection analysis. | — |
 | **Multiperson** | Scenes where more than one person is visible simultaneously. | — |
-| **Partial Occlusion** | — | Scenes where one or both individuals are partially hidden by environmental features. |
-| **Full Occlusion** | — | Scenes where one or both individuals are completely obscured from view. |
+| **Partial Occlusion** | — | Scenes where one individual is completely occluded by environmental features. |
+| **Full Occlusion** | — | Scenes where both individuals are completely occluded, in at least one viewpoint. |
 
 ## Frame Counts
 
@@ -120,7 +124,14 @@ Each aerial and ground platform folder contains the following topics:
 
 ## Citation
 
-> Citation will be added upon publication.
+```bibtex
+@article{co-glance2026,
+  title={Co-GLANCE: Uncertainty-Aware Active Perception for Heterogeneous Robot Teaming},
+  author={Redacted until publication},
+  journal={Redacted until publication},
+  year={2026}
+}
+```
 
 ## License