---
license: cc-by-4.0
task_categories:
  - image-to-image
  - visual-question-answering
tags:
  - loop-closure
  - visual-odometry
  - slam
  - autonomous-vehicles
  - gps
  - dashcam
  - place-recognition
pretty_name: Motive Loop Closure Dataset
size_categories:
  - 1K<n<10K
---

# Motive Loop Closure Dataset

A fleet-scale collection of dashcam clips and GPS telemetry capturing **genuine driving loops** — routes where a vehicle returned to within metres of a prior location after driving away. Designed as a benchmark and training resource for loop closure detection, visual place recognition (VPR), and visual odometry (VO) drift characterisation.

## Dataset Description

### Summary

Each entry in this dataset is a **loop sequence**: a pair of 3-minute dashcam clips from the same vehicle — one captured on the vehicle's **first pass** through a location (origin), and one captured on the vehicle's **return** (revisit), anywhere from minutes to several hours later. Ground-truth revisit distance is measured by GPS.

The dataset covers urban and mixed driving across a commercial freight fleet in Accra, Ghana, collected via the [Motive](https://gomotive.com) fleet management platform.

### Source data

- **Vehicles**: 5 vehicles (anonymised as v101, v102, v103, v106, v113)
- **Collection period**: September 2026
- **Camera**: Front-facing dashcam, 1920×1080, 29.97 fps
- **GPS**: ~1 Hz fixes from the Motive vehicle location API

### Loop detection method

Candidate loop pairs were identified by scanning GPS traces for fix pairs where:
- Temporal gap > 30 s (vehicle had moved away)
- Haversine revisit distance < 50 m
- Path length ≥ 300 m (filters depot/stationary false positives)
- Speed > 1 m/s at both endpoints

Candidates were ranked by `revisit_m / duration_s` (lower = tighter return over longer drive). The top 33 candidates with confirmed video availability were recalled and are included here.

## Dataset Structure

```
README.md
extract_frames.py          # Run locally to extract full frames from raw videos
metadata/
  loop_pairs.parquet       # One row per loop sequence — key statistics
  metadata.jsonl           # One record per clip — GPS endpoints + loop pair reference
data/
  v102_20260909_loop01/
    origin/
      preview/             # 5 evenly-spaced JPEGs for quick visual inspection
        000001.jpg ... 000005.jpg
      telemetry.csv        # timestamp_utc, lat, lon, speed_mph, heading_deg
    revisit/
      preview/
      telemetry.csv
  v113_20260909_loop01/
    ...
videos/
  v102_origin_20260909_183645_front_facing.mp4   # raw 3-min clips (180 s, 29.97 fps)
  v102_revisit_20260909_231842_front_facing.mp4
  ...
```

### Extracting full frames

The dataset ships 5 preview frames per clip to keep download size small. To extract all frames at any frame rate:

```bash
# Clone or download the dataset, then:
python3 extract_frames.py              # 2 fps (default), all sequences
python3 extract_frames.py --fps 5     # 5 fps
python3 extract_frames.py --sequence v102_20260909_loop01 --fps 10
```

Requires **ffmpeg** on PATH. Frames land in `data/<sequence>/<role>/frames/<fps>fps/`.

### `metadata/loop_pairs.parquet` schema

| Column | Type | Description |
|---|---|---|
| `sequence_id` | string | e.g. `v102_20260909_loop01` |
| `vehicle` | string | Anonymised vehicle ID |
| `origin_clip_start_utc` | ISO datetime | Start of the origin clip |
| `revisit_clip_start_utc` | ISO datetime | Start of the revisit clip |
| `revisit_gps_m` | float | GPS distance between loop endpoints (metres) |
| `loop_duration_s` | float | Elapsed seconds between origin and revisit |
| `origin_video` | string | Filename in `videos/` |
| `revisit_video` | string | Filename in `videos/` |

### `metadata/metadata.jsonl` schema

One record per clip (2 per loop sequence: origin + revisit).

```json
{
  "clip_id":         "v102_20260909_loop01_origin",
  "sequence_id":     "v102_20260909_loop01",
  "role":            "origin",
  "vehicle":         "102",
  "clip_start_utc":  "2026-09-09T18:36:45+00:00",
  "clip_duration_s": 180,
  "video_path":      "videos/v102_origin_20260909_183645_front_facing.mp4",
  "telemetry_path":  "data/v102_20260909_loop01/origin/telemetry.csv",
  "preview_frames":  ["data/v102_20260909_loop01/origin/preview/000001.jpg", "..."],
  "gps_start":       {"lat": 5.6037, "lon": -0.1870},
  "gps_end":         {"lat": 5.6041, "lon": -0.1873},
  "loop_partner": {
    "clip_id":         "v102_20260909_loop01_revisit",
    "revisit_gps_m":   0.53,
    "loop_duration_s": 17007
  }
}
```

> **Note**: Visual similarity scores between origin/revisit clip pairs are not included in v1.0. GPS distance between loop endpoints is the ground-truth signal. Community contributions of VPR similarity annotations (NetVLAD, DINOv2, SuperPoint+SuperGlue, etc.) are welcome.

## Intended Uses

| Use case | What to use |
|---|---|
| Loop closure detection benchmark | `loop_pairs.parquet` — pair origin/revisit sequences, test if your detector fires |
| Visual place recognition evaluation | Frame pairs across `origin` / `revisit` roles; GPS as ground truth |
| VO drift characterisation | Full clip sequences with GPS; measure accumulated drift at closure |
| Self-supervised descriptor training | GPS-supervised positive pairs (same location, hours apart) |
| HD map staleness detection | Same route, different dates — detect scene changes |

## Limitations

- **Single camera**: Front-facing only. No stereo, no lidar.
- **GPS accuracy**: ~1–10 m typical for commercial fleet GPS. Revisit distances below ~2 m should be treated as "sub-metre" rather than exact.
- **No visual similarity ground truth**: Frame-level correspondence labels are not provided in v1.0.
- **Weather / time-of-day**: Not labelled; inferred from timestamps.
- **v110 footage**: All recalls for vehicle 110 timed out on the Motive platform and are absent from this dataset.

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{motive_loop_closure_2026,
  title   = {Motive Loop Closure Dataset},
  year    = {2026},
  note    = {HuggingFace Datasets},
  url     = {https://huggingface.co/datasets/<username>/motive-loop-closure},
  license = {CC BY 4.0}
}
```

## License

[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)