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Please cite the original MOTChallenge work and follow the original dataset terms and conditions. + +## Dataset Details + +### Dataset Description + +- **Dataset name:** MOT17 +- **Task:** Multi-object tracking, pedestrian tracking, object detection +- **Domain:** Surveillance / street / public pedestrian scenes +- **Data type:** Image sequences with annotations and detections +- **Original benchmark:** [MOTChallenge MOT17](https://motchallenge.net/data/MOT17/) +- **Paper:** [MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking](https://arxiv.org/abs/2010.07548) + +MOT17 contains the same underlying image sequences as MOT16, but with updated ground truth and detector outputs. Each sequence is supplied with detections from DPM, Faster R-CNN, and SDP, which enables comparison of trackers under different detection-quality settings. + +### Supported Tasks + +This dataset can be used for: + +- Multiple Object Tracking (MOT) +- Multi-pedestrian tracking +- Tracking-by-detection research +- Pedestrian detection +- Re-identification-assisted tracking +- MOT benchmark conversion and evaluation pipelines + +## Dataset Structure + +A typical MOT17 repository follows the official MOTChallenge folder layout: + +```text +MOT17/ +├── train/ +│ ├── MOT17-02-DPM/ +│ ├── MOT17-02-FRCNN/ +│ ├── MOT17-02-SDP/ +│ ├── MOT17-04-DPM/ +│ ├── ... +│ └── MOT17-13-SDP/ +├── test/ +│ ├── MOT17-01-DPM/ +│ ├── MOT17-01-FRCNN/ +│ ├── MOT17-01-SDP/ +│ ├── ... +│ └── MOT17-14-SDP/ +└── README.md +``` + +Each sequence directory typically contains: + +```text +MOT17-XX-DET/ +├── img1/ # Video frames as image files +├── det/ # Public detections +│ └── det.txt +├── gt/ # Ground-truth annotations; training split only +│ └── gt.txt +└── seqinfo.ini # Sequence metadata +``` + +Where `DET` is one of: + +- `DPM` +- `FRCNN` +- `SDP` + +## Splits + +### Training Sequences + +The MOT17 training split contains 7 base video sequences, each provided with DPM, FRCNN, and SDP detections, resulting in 21 sequence-detector folders. + +Base training sequences: + +- `MOT17-02` +- `MOT17-04` +- `MOT17-05` +- `MOT17-09` +- `MOT17-10` +- `MOT17-11` +- `MOT17-13` + +### Test Sequences + +The MOT17 test split contains 7 base video sequences, each provided with DPM, FRCNN, and SDP detections, resulting in 21 sequence-detector folders. + +Base test sequences: + +- `MOT17-01` +- `MOT17-03` +- `MOT17-06` +- `MOT17-07` +- `MOT17-08` +- `MOT17-12` +- `MOT17-14` + +## Annotation Format + +MOT17 uses the standard MOTChallenge comma-separated text format. + +### Ground Truth Format + +Training annotations are stored in: + +```text +gt/gt.txt +``` + +Each row generally follows: + +```text +, , , , , , , , +``` + +Field descriptions: + +| Field | Description | +|---|---| +| `frame` | Frame index, starting from 1 | +| `id` | Object identity ID | +| `bb_left` | Left coordinate of bounding box | +| `bb_top` | Top coordinate of bounding box | +| `bb_width` | Bounding-box width | +| `bb_height` | Bounding-box height | +| `conf` | Confidence flag for ground truth | +| `class` | Object class label | +| `visibility` | Visibility ratio / visibility flag | + +### Detection Format + +Public detections are stored in: + +```text +det/det.txt +``` + +Each row generally follows: + +```text +, , , , , , , , , +``` + +For detection files, `id` is commonly set to `-1`, and the final world-coordinate fields may be unused depending on the detector or sequence. + +## Usage + +### Download from Hugging Face + +```python +from huggingface_hub import snapshot_download + +repo_dir = snapshot_download( + repo_id="YOUR_USERNAME_OR_ORG/MOT17", + repo_type="dataset", +) + +print(repo_dir) +``` + +Replace `YOUR_USERNAME_OR_ORG/MOT17` with the actual Hugging Face dataset repository ID. + +### Example: Read MOTChallenge Annotations with Python + +```python +from pathlib import Path +import pandas as pd + +seq_dir = Path("MOT17/train/MOT17-02-FRCNN") +gt_path = seq_dir / "gt" / "gt.txt" + +gt = pd.read_csv( + gt_path, + header=None, + names=[ + "frame", + "id", + "bb_left", + "bb_top", + "bb_width", + "bb_height", + "conf", + "class", + "visibility", + ], +) + +print(gt.head()) +``` + +### Example: Iterate Over Frames + +```python +from pathlib import Path + +img_dir = Path("MOT17/train/MOT17-02-FRCNN/img1") +frames = sorted(img_dir.glob("*.jpg")) + +print(f"Number of frames: {len(frames)}") +print(frames[:5]) +``` + +## Evaluation + +For official MOTChallenge-style evaluation, use the MOTChallenge evaluation protocol and compatible tools such as TrackEval. + +Typical predicted tracking result format: + +```text +, , , , , , , , , +``` + +Common MOT metrics include: + +- MOTA +- MOTP +- IDF1 +- HOTA +- FP / FN / ID switches +- Mostly Tracked / Mostly Lost trajectories + +Official benchmark submissions should be made through the MOTChallenge platform, not through this Hugging Face repository. + +## Intended Use + +This dataset is intended for: + +- Academic and industrial research in multi-object tracking +- Benchmarking MOT algorithms +- Studying detector quality and its impact on tracking +- Developing tracking-by-detection pipelines +- Training and validating pedestrian tracking systems + +## Limitations and Responsible Use + +MOT17 contains real-world pedestrian scenes. Users should consider privacy, surveillance, and fairness implications when training or deploying models using this dataset. + +Known limitations include: + +- The dataset focuses on pedestrian-heavy urban and public scenes. +- Tracking performance may vary strongly with detector choice. +- The dataset is not representative of all countries, camera types, lighting conditions, or pedestrian demographics. +- Test-set ground truth is not included in the public dataset release. +- Models trained on MOT17 should be evaluated carefully before deployment in real-world surveillance or safety-critical systems. + +## Licensing and Redistribution + +The license metadata for this Hugging Face dataset card is set to: + +```yaml +license: other +``` + +Please refer to the original MOTChallenge website for the authoritative dataset access terms, redistribution rules, and citation requirements. Do not assume this mirror grants rights beyond those provided by the original dataset owners. + +## Citation + +Please cite the MOTChallenge benchmark paper if you use this dataset: + +```bibtex +@article{dendorfer2020motchallenge, + title={MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking}, + author={Dendorfer, Patrick and Osep, Aljosa and Milan, Anton and Schindler, Konrad and Cremers, Daniel and Reid, Ian and Roth, Stefan and Leal-Taixe, Laura}, + journal={International Journal of Computer Vision}, + year={2020}, + doi={10.1007/s11263-020-01393-0} +} +``` + +You may also cite the earlier MOT16/MOTChallenge references listed on the official MOT17 page when appropriate. + +## References + +- MOTChallenge MOT17: https://motchallenge.net/data/MOT17/ +- MOTChallenge paper: https://arxiv.org/abs/2010.07548 +- MOTChallenge website: https://motchallenge.net/ diff --git a/ablation/MOT17-02-FRCNN/det/det.txt b/ablation/MOT17-02-FRCNN/det/det.txt new file mode 100644 index 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