Co-GLANCE Dataset

Dataset Summary

Co-GLANCE 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.

Scenarios

The dataset is organized into two collection scenarios:

	Construction Scenario (2025-03-30)	Camouflage Scenario (2025-04-14)
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)
Aerial Hardware	Arducam HQ IMX477	GoPro HERO 10
No. of Runs	4	3

Preview

Data Categories

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.
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.

Frame Counts

Per-run frame counts for each scene category. The dataset contains 2,071 annotated frame pairs in total.

Construction Scenario (03-30)

Run	Clean	Filtered out	Multiperson	All
1	77	34	7	118
2	229	43	54	326
3	88	54	138	280
4	135	144	206	485
Total	529	275	405	1209

Camouflage Scenario (04-14)

Run	Clean	Partial Occlusion	Full Occlusion	All
1	54	101	31	186
2	210	252	83	545
3	69	47	15	131
Total	333	400	129	862

Dataset Structure

dataset/
├── 03-30/                        # Construction Scenario
│   └── <run>/                    # 4 runs
│       ├── all/
│       ├── clean/
│       ├── filtered_out/
│       └── multiperson/
│           ├── aerial/
│           │   ├── rgb/          # PNG frames
│           │   ├── gt_bbox/      # Frames with bounding boxes rendered on image
│           │   ├── acceleration/ # JSON sensor stream
│           │   ├── attitude/     # JSON sensor stream
│           │   ├── gps_position/ # JSON RTK GPS stream
│           │   └── *.json        # Additional odometry data
│           └── ground/
│               └── <same structure>
└── 04-14/                        # Camouflage Scenario
    └── <run>/                    # 3 runs
        ├── all/
        ├── clean/
        ├── partial_occlusion/
        └── full_occlusion/
            ├── aerial/
            │   └── <same structure>
            └── ground/
                └── <same structure>

Data Fields

Each aerial and ground platform folder contains the following topics:

Folder / File	Format	Description
rgb/	PNG	Raw RGB frames from the platform camera
gt_bbox/	PNG	Corresponding frames with ground-truth bounding boxes rendered on the image
acceleration/	JSON	IMU linear acceleration stream
attitude/	JSON	Platform orientation / attitude stream
gps_position/	JSON	RTK GPS position stream
*.json	JSON	Additional odometry and platform state data

Citation

Citation will be added upon publication.

License

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).