MOT17

MOT17 is a benchmark dataset for single-camera multi-object tracking (MOT), focused primarily on pedestrian tracking in real-world video sequences. This Hugging Face repository provides the MOT17 data in the original MOTChallenge-style structure for research, benchmarking, training, and evaluation of multi-object tracking systems.

MOT17 extends MOT16 with more accurate ground-truth annotations and provides each sequence with three public detection sets:

• DPM
• Faster R-CNN / FRCNN
• SDP

This repository is a convenience mirror/repackaging of MOT17. Please cite the original MOTChallenge work and follow the original dataset terms and conditions.

Dataset Details

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:

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:

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:

gt/gt.txt

Each row generally follows:

<frame>, <id>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <conf>, <class>, <visibility>

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:

det/det.txt

Each row generally follows:

<frame>, <id>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <conf>, <x>, <y>, <z>

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

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

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

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:

<frame>, <id>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <conf>, <x>, <y>, <z>

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:

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:

@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/