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---
license: unknown
pretty_name: NJU-HARD-Tracking
task_categories:
- other
tags:
- remote-sensing
- uav
- aerial-imagery
- ultra-high-resolution
- parquet
- multi-object-tracking
- object-tracking
size_categories:
- 1K<n<10K
configs:
- config_name: default
default: true
data_files:
- split: train
path: data/train-*.parquet
- split: validation
path: data/validation-*.parquet
- split: test
path: data/test-*.parquet
- config_name: preview
data_files:
- split: sample
path: preview/*-preview.parquet
---
# NJU-HARD-Tracking
<table align="center" role="presentation" style="margin:0 auto; border:0; background:transparent;">
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<img src="./assets/rl-mind-logo-v2.webp" alt="RL-MIND research group logo" width="160" height="160" loading="eager" fetchpriority="high" decoding="async" style="width:160px; height:160px; object-fit:contain;" />
</td>
<td align="center" style="border:0; background:transparent; padding:0 8px;">
<img src="./assets/dataset-logo-v2.webp" alt="NJU-HARD-Tracking dataset logo" width="160" height="160" loading="eager" fetchpriority="high" decoding="async" style="width:160px; height:160px; object-fit:contain;" />
</td>
</tr>
</table>
<p align="center"><b>Multi-Object Tracking across 122 MP UAV Image Sequences</b></p>
<p align="center"><a href="https://huggingface.co/datasets/RL-MIND/NJU-HARD-Tracking">🤗 Hugging Face</a> · <a href="https://www.modelscope.cn/datasets/KAIWANG/NJU-HARD-Tracking">🟣 ModelScope</a> · 📊 Statistics: <a href="https://huggingface.co/datasets/RL-MIND/NJU-HARD-Tracking/blob/main/statistics.json">HF</a> / <a href="https://www.modelscope.cn/datasets/KAIWANG/NJU-HARD-Tracking/file/view/master/statistics.json?status=1">MS</a></p>
<p align="center"><b>English</b> | 中文: <a href="https://huggingface.co/datasets/RL-MIND/NJU-HARD-Tracking/blob/main/README_ZH.md">Hugging Face</a> · <a href="https://www.modelscope.cn/datasets/KAIWANG/NJU-HARD-Tracking/file/view/master/README_ZH.md?status=1">ModelScope</a></p>
## 🌍 Overview
**NJU-HARD-Tracking** provides the **multi-object-tracking release of HARD**, with full-resolution frames, temporal ordering, and the original instance annotations. It supports studying how detection and association behave across wide-area aerial scenes.
The source contains **3,549 original JPEG frames**, **21 sequences**, and **138,830 box annotations** across four classes: pedestrian, car, bus, and truck. Every frame is **12,768 × 9,564 pixels** (approximately **122 MP**). The imagery covers sparse and dense scenes, with substantial variation in target scale and camera motion.
HARD stands for **Ultra-High-resolution Airborne Remote-sensing Dataset**. The [NJU-HARD-Detection release](https://huggingface.co/datasets/RL-MIND/NJU-HARD-Detection) contains the **same frame and annotation records** with a task-specific card. Both releases preserve the original JPEG bytes and labels. Their repository names identify these task releases; they do not denote separate acquisitions.
## ✨ What the Data Covers
- **Wide-area detail:** complete 122 MP frames retain small pedestrian and vehicle targets within a broad field of view.
- **Temporal structure:** 21 ordered sequences connect per-frame localization with cross-frame association.
- **Diverse viewing conditions:** the source manuscript describes 50–355 m acquisition heights and four flight modes: linear-nadir, linear-oblique, stationary-gaze, and orbit-gaze.
- **Realistic imbalance:** cars dominate the annotations, while buses and trucks are less frequent. Scene density ranges from approximately 12.4 to 79.8 annotated objects per frame across the three capture subsets.
These properties support research on wide-area spatio-temporal scene understanding. They do not establish a performance claim for any model.
## 📊 Dataset at a Glance
| Item | Source inventory represented by the release |
|:---|:---|
| Original frames | 3,549 |
| Image resolution | 12,768 × 9,564 pixels |
| Capture subsets / sequences | 3 / 21 |
| Box annotation rows | 138,830 |
| Frames with a source label file | 3,544 |
| Frames without a source label file | 5; annotation state is missing |
| Source JPEG payload | 368,182,969,599 bytes, approximately 368.18 GB |
| Storage unit | One original frame per Parquet row |
| Configuration | `default` |
| Splits | `train`, `validation`, `test` |
| Separate browsing preview | `preview` / `sample`: 7 thumbnail frames in separate Parquet files, at most 1,024 pixels on the longest edge |
The JPEG byte count describes the original image payload, not the final Parquet file size. In `default`, each image embeds its original `bytes` and has `path=null`; it is not resized, cropped, or re-encoded. The separate seven-frame `preview` uses derived thumbnails and is not included in the 3,549-frame total. It spans the three subsets and splits, including examples with missing labels, repeated identities, and out-of-bounds boxes. Box totals count annotation rows across frames, not distinct physical objects or verified unique trajectories.
| Dense `category` | Class | Original `source_class_id` | Annotation rows |
|---:|:---|---:|---:|
| 0 | pedestrian | 0 | 27,711 |
| 1 | car | 2 | 103,361 |
| 2 | bus | 5 | 4,033 |
| 3 | truck | 7 | 3,725 |
The four source-ID totals match the manuscript's per-class totals exactly. `category` provides a contiguous label space; `source_class_id` preserves the original 0/2/5/7 values.
## 🗂️ Sequence Splits
The releases use the sequence-level split in the manuscript's Table 4. All frames from a sequence stay together. The mapping from the manuscript's A/B/C subset codes to source directories was reconstructed from sequence inventories and verified by exact agreement with all three split annotation totals.
| Split | Sequences | Frames | Annotation rows |
|:---|---:|---:|---:|
| `train` | 13 | 2,055 | 89,148 |
| `validation` | 3 | 551 | 15,820 |
| `test` | 5 | 943 | 33,862 |
| Source directory / paper code | `train` sequence IDs | `validation` | `test` |
|:---|:---|:---|:---|
| `UAV-BASE-13` / A | 1, 2, 3, 4, 5 | 6 | — |
| `UAV-BASE-14` / B | 1, 2, 5, 8, 9 | 3, 7 | 4, 6 |
| `NJU-xianlin` / C | 1, 5, 6 | — | 2, 3, 4 |
The split is **sequence-disjoint, not site-disjoint**. The manuscript's tracking evaluation pools the **five test sequences** with TrackEval's combined convention. The complete tracking release includes all 21 sequences for training, validation, and testing.
## 🧩 Record Format
Each Parquet row is one frame. The detection and tracking releases share this schema so that image bytes, labels, and source provenance remain comparable.
| Field | Meaning |
|:---|:---|
| `image`, `preview_only` | Native image value with a null path; original JPEG in `default`, a derived thumbnail when `preview_only=true` |
| `image_id`, `scene`, `sequence_id` | Frame identity, capture subset, and sequence identity |
| `source_sequence_id`, `paper_sequence_id` | Original sequence number and corresponding A/B/C manuscript sequence code |
| `frame_id`, `frame_name` | Integer parsed from the original frame filename, and that filename; the original starting index is retained |
| `split`, `width`, `height` | Assigned source-sequence split and dimensions of the current `image.bytes` |
| `source_width`, `source_height` | Original full-resolution dimensions, also retained in the preview |
| `source_image_path`, `source_image_sha256` | Relative source path and original JPEG checksum |
| `image_sha256` | Checksum of the current `image.bytes`; identical to the source checksum in `default`, different for a derived preview |
| `annotation_status`, `num_objects` | `present` or `missing`; object count is null when the source label is missing |
| `source_annotation_path`, `raw_annotation_bytes`, `annotation_sha256` | Original label provenance and exact bytes; bytes/checksum are null for missing labels |
| `quality_flags` | Frame-level source-quality indicators |
| `objects` | List of object records, or **null** for a missing source label |
Each object includes:
| Field | Meaning |
|:---|:---|
| `id`, `source_line` | Source label path plus row identity, and the original line number |
| `category`, `source_class_id` | Contiguous class label and unchanged source class ID |
| `source_track_id`, `source_track_key` | Raw instance ID and its sequence/class-scoped key |
| `source_bbox_normalized` | Original YOLO-style `[cx, cy, width, height]` in normalized coordinates |
| `bbox`, `area` | COCO-style `[x_min, y_min, width, height]` and area in the current image's pixels, retained as floating point |
| `quality_flags` | Object-level source-quality indicators |
For tracking, process frames within each `sequence_id` in increasing `frame_id` order. `source_track_key` includes the sequence, source class, and raw instance ID; it prevents cross-class ID collisions but does not resolve the same-class ambiguities listed below. Preserve `source_track_id` when exporting or comparing source labels.
Pixel boxes are derived from the original normalized center/size coordinates and the current `width`/`height`. They use original-image pixels in `default` and thumbnail pixels in `preview`; they are **not clipped or rounded to integers**. No actual capture timestamp or measured video frame rate is inferred from the frame index. The manuscript's `P = 2 s` is recorded as evaluation context, not synthesized per-frame timing metadata.
## ⚠️ Source Annotation Quality
The release preserves the audited source rather than silently changing ambiguous annotations. Quality flags and original label bytes make the following issues visible:
| Source issue | Count / interpretation |
|:---|:---|
| Missing label files | 5 frames; `objects=null`, not an empty list or a confirmed negative frame |
| Raw IDs reused across classes within a sequence | 158 sequence/ID groups; do not use raw ID alone as a track key |
| Same class and ID repeated within a frame | 24 groups; a class-scoped key still has source ambiguity in these cases |
| Exact duplicate annotation row | 1 duplicate occurrence, retained |
| Box corners beyond normalized image bounds | 2,149 rows at a tolerance of `1e-6`; original coordinates retained |
The missing label files correspond to `UAV-BASE-13` sequence 1 frame 0, sequence 2 frame 0, and sequence 6 frames 0–2. There are **no empty source TXT files**. Downstream experiments should state how they treat missing labels, duplicate identities, duplicate rows, and out-of-bounds boxes. A cleaned evaluation protocol must document its transformations separately; the source track keys are not a claim of fully resolved ground-truth identities.
## 🚀 Quick Start
Start with the small `preview` to inspect the schema and quality flags. With Hugging Face `datasets`, streaming and disabled image decoding keep this inspection lightweight:
```python
from datasets import Image, load_dataset
data = load_dataset(
"RL-MIND/NJU-HARD-Tracking",
name="preview",
split="sample",
streaming=True,
).cast_column("image", Image(decode=False))
frame = next(iter(data))
print(frame["sequence_id"], frame["frame_id"], frame["annotation_status"])
if frame["objects"] is None:
print("Missing source label; this is not a negative frame.")
else:
for obj in frame["objects"]:
print(obj["source_track_key"], obj["bbox"], obj["quality_flags"])
```
ModelScope's dataset loader exposes the same preview:
```python
from modelscope.msdatasets import MsDataset
data = MsDataset.load(
"KAIWANG/NJU-HARD-Tracking",
subset_name="preview",
split="sample",
use_streaming=True,
)
frame = next(iter(data))
print(frame["sequence_id"], frame["frame_id"], frame["annotation_status"])
```
For original frames, use `name="default", split="test"` on Hugging Face, or `subset_name="default", split="test"` on ModelScope; `train` and `validation` are also available. The preview is a browsing aid, **not a continuous tracking evaluation sequence**. Use `default` for full-resolution experiments and chronological tracking. Full-resolution frames remain large even when streamed; after resizing an image, transform its boxes consistently.
## 📄 License and Research Context
License metadata is **unknown**. The source manuscript describes research-only distribution and prohibits attempts to re-identify individuals or vehicles, but a complete license agreement is not supplied with the source material reviewed for this release.
The detection task uses per-frame box supervision; the tracking task additionally studies sequence-local association. The manuscript reports COCO detection metrics and HOTA/s-HOTA tracking evaluation. This data package does not, by itself, establish a validated implementation of the manuscript's latency-accounting protocol.