| --- |
| license: other |
| task_categories: |
| - object-detection |
| - image-segmentation |
| tags: |
| - traffic |
| - aerial |
| - drone |
| - roadside |
| - multi-view |
| - coco |
| - instance-segmentation |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # DLR v1 — 2D detection + instance segmentation |
|
|
| Multi-view roadside + drone traffic imagery from three German sites, with 2D |
| boxes, per-instance masks and class labels in COCO format. |
|
|
| Each **position** is a time-synchronized pair: a **drone** bird's-eye view |
| (3840×2160) and one or more fixed **Axis** roadside cameras (1920×960) watching |
| the same intersection. |
|
|
| ## Contents |
|
|
| ``` |
| train/ _annotations.coco.json + <run_key>/*.jpg (34 subdirs, 18015 images) |
| valid/ _annotations.coco.json + *.jpg (5892 images) |
| test/ _annotations.coco.json + *.jpg (5880 images) |
| ``` |
|
|
| | split | images | instances | source runs | |
| |---|---|---|---| |
| | train | 18,015 | 375,926 | 34 | |
| | valid | 5,892 | 104,024 | 24 | |
| | test | 5,880 | 105,952 | 24 | |
| | **total** | **29,787** | **585,902** | 58 | |
|
|
| `train/` images are grouped into one subdirectory per source run because the Hub |
| caps a directory at 10,000 files. This is transparent to loaders: COCO |
| `file_name` is a relative path, so `os.path.join(split_dir, file_name)` resolves |
| correctly in all three splits. |
|
|
| ## Classes |
|
|
| | id | name | instances | |
| |---|---|---| |
| | 0 | person | 0 | |
| | 1 | bicycle | 0 | |
| | 2 | car | 528,354 | |
| | 3 | motorcycle | 16,048 | |
| | 4 | bus | 3,202 | |
| | 5 | truck | 12,549 | |
| | 6 | van | 25,749 | |
|
|
| `person` and `bicycle` are kept for COCO id compatibility but carry **no |
| annotations** — the prompt set targets vehicles only. `van` is id 6 so the |
| standard COCO ids 0–5 stay stable. |
|
|
| Traffic is overwhelmingly cars (90%); `bus` is rare (0.5%). This is a |
| long-tailed vehicle dataset, not a balanced one. |
|
|
| ## Format |
|
|
| Standard COCO. Detection and instance segmentation share one file — masks are |
| compressed RLE in `segmentation`. |
|
|
| ```jsonc |
| { |
| "id": 1, "image_id": 0, "category_id": 2, |
| "bbox": [x, y, w, h], // xywh, pixels |
| "area": 3896, // == mask pixel count, not bbox area |
| "iscrowd": 0, |
| "segmentation": {"size": [h, w], "counts": "..."}, // full-frame RLE |
| "score": 0.96, // detection confidence |
| "track_id": 1474, // stable within its source run |
| "global_id": "sb_pos3_drone_0001_1474" |
| } |
| ``` |
|
|
| Loads with `pycocotools`; RLE round-trips (`area == mask.sum()` verified on |
| every split). |
|
|
| `global_id` is **clip-scoped** (`"<run>_<track_id>"`) — it identifies a vehicle |
| within one camera, not across cameras. |
|
|
| Image file names are `<run_key>_<source_frame_index>.jpg`, so every image is |
| traceable to its source video and frame. |
|
|
| ```python |
| import os |
| from pycocotools.coco import COCO |
| |
| split = "train" |
| coco = COCO(f"{split}/_annotations.coco.json") |
| img = coco.loadImgs(coco.getImgIds()[0])[0] |
| path = os.path.join(split, img["file_name"]) # works for every split |
| anns = coco.loadAnns(coco.getAnnIds(imgIds=img["id"])) |
| mask = coco.annToMask(anns[0]) # HxW binary |
| ``` |
|
|
| ## Splits |
|
|
| Split **by position**, then a frame-level cut — not a random shuffle. Randomly |
| splitting video frames leaks near-identical neighbours across splits and |
| inflates scores. |
|
|
| - **train** positions are exclusive: their frames never appear in valid/test. |
| - **eval** positions feed both valid and test, cut temporally (first part → |
| valid, remainder → test). valid and test may share a position; neither shares |
| one with train. |
|
|
| Frames are selected with a gap + min-vehicle rule rather than a fixed stride: |
| keep a frame only if it has ≥3 annotations and is ≥25 source frames (~1 s) after |
| the last kept one, which drops near-duplicate consecutive frames. |
|
|
| Verified: **0 source-run overlap between train and eval**. |
|
|
| ### Caveat — Tostmannplatz |
|
|
| At Tostmannplatz the split is per *camera*: `tp_pos1`'s drone and `cam_3_100` |
| are in eval while its `cam_48_101`/`cam_48_106` are in train. Those cameras |
| observe **the same intersection at the same instant** from different viewpoints, |
| so the same traffic appears on both sides of the split, seen from different |
| angles. Exclude the `tp_pos1_*` runs from train if you need a strictly |
| scene-disjoint evaluation. |
|
|
| ## Notes |
|
|
| - Strongly class-imbalanced (90% car). |
| - `person`/`bicycle` are empty by construction. |
| - Frames within a split are temporally correlated (~1 s apart within a run). |
| - Three sites only; limited weather and lighting diversity. |
|
|
| Contact: Javi Borau — `jborau@caos.uc3m.es` |
|
|
