MMOT

MMOT is a large-scale benchmark for drone-based multispectral multi-object tracking (MOT). It is designed for tracking multiple objects in challenging UAV scenes using 8-band multispectral imagery and oriented bounding-box (OBB) annotations.

This Hugging Face repository provides MMOT data for research, benchmarking, training, and evaluation of multispectral and rotation-aware multi-object tracking systems.

This repository is a Hugging Face packaging of MMOT. Please cite the original MMOT paper and follow the official dataset license and redistribution terms.

Dataset Details

MMOT was introduced to address the lack of dedicated UAV multispectral MOT datasets. The benchmark emphasizes conditions where RGB-only spatial appearance cues can be weak, including extreme small targets, dense instances, severe occlusion, blur, cluttered backgrounds, fast object motion, irregular UAV motion, and in-plane rotation.

Supported Tasks

This dataset can be used for:

• Multispectral multi-object tracking
• Drone-based / UAV multi-object tracking
• Multi-class tracking in aerial scenes
• Oriented bounding-box detection and tracking
• Rotation-aware tracking-by-detection
• Rotation-aware tracking-by-query
• Multispectral feature learning
• Tracking association under small-object and high-density conditions
• MOT benchmark conversion and evaluation pipelines

Dataset Structure

The Hugging Face release packages video sequences as .tar archives. Each archive contains multispectral .npy frames and per-frame .txt annotations.

A typical Hugging Face layout is:

MMOT_DATASET/
├── train/
│   ├── data30-8.tar
│   ├── data23-2.tar
│   └── ...
├── test/
│   ├── dataXX-X.tar
│   └── ...
└── README.md

After extraction, each sequence archive contains frame-level files such as:

data30-8/
├── 000001.npy
├── 000001.txt
├── 000002.npy
├── 000002.txt
└── ...

The official repository includes a helper script that converts the Hugging Face archive layout into the standard MMOT directory layout:

python dataset/huggingface_tar_to_standard.py --root /path/to/MMOT_DATASET

After conversion, the expected standard layout is:

MMOT_DATASET/
├── train/
│   ├── npy/
│   │   ├── data23-2/
│   │   │   ├── 000001.npy
│   │   │   ├── 000002.npy
│   │   │   └── ...
│   │   └── ...
│   └── mot/
│       ├── data23-2.txt
│       ├── data23-3.txt
│       └── ...
└── test/
    ├── npy/
    │   └── ...
    └── mot/
        └── ...

Each standard-layout sequence contains:

npy/SEQ_NAME/       # Multispectral frames as .npy arrays
mot/SEQ_NAME.txt    # Merged sequence-level MOT-style annotations

Splits

MMOT is split into training and test sets. The paper reports that the split avoids overlap of geographic locations and specific scene instances across subsets.

Split	Sequences	Frames	Identity tracks	Oriented boxes
train	75	8,372	6,101	~292K
test	50	5,446	4,527	~196K
Total	125	13,818	10,628	~488K

Dataset Statistics

The MMOT paper and official repository report the following high-level statistics:

Statistic	Value
Video sequences	125
Frames	13.8K
Total duration	2,767 seconds
Oriented bounding-box annotations	~488.8K
Average annotations per frame	35.2
Object categories	8
Spectral channels	8
Spatial resolution	1200 × 900
Flight altitude range	80–200 m
Maximum objects per frame	155
Average objects within 300 px radius	19.4

Categories

MMOT contains three superclasses and eight fine-grained object categories:

Superclass	Categories
HUMAN	pedestrian
VEHICLE	car, van, truck, bus
BIKE / BICYCLE	bike, tricycle, awning-bike

The official TrackEval configuration uses the following class IDs:

Class ID	Class name
0	car
1	bike
2	pedestrian
3	van
4	truck
5	bus
6	tricycle
7	awning-bike

Annotation Format

MMOT uses MOT-style text files with oriented bounding-box coordinates.

Sequence-Level MOT-OBB Format

After conversion to the standard layout, annotations are stored as:

train/mot/SEQ_NAME.txt
test/mot/SEQ_NAME.txt

Each row is comma-separated and follows the format used by the official MMOT tools:

<frame>, <id>, <x0>, <y0>, <x1>, <y1>, <x2>, <y2>, <x3>, <y3>, <score_or_flag>, <class>, <truncation_or_extra_flag>

Field descriptions:

Field	Description
frame	Frame index, starting from 1
id	Object identity ID within the sequence
x0, y0, ..., x3, y3	Four OBB corner points in image coordinates
score_or_flag	Score, confidence, or validity field depending on ground-truth vs. prediction files
class	Category ID, using the class mapping above
truncation_or_extra_flag	Extra annotation flag; commonly used for truncation-related metadata

The eight corner-coordinate fields represent the oriented bounding box directly. For code that expects rotated rectangles such as (cx, cy, w, h, angle), convert these corner points using your preferred OBB geometry library.

YOLO-OBB Conversion Format

The official repository includes a mot2yolo_obb.py converter. Its YOLO-OBB output rows use normalized corner coordinates:

<class> <x0> <y0> <x1> <y1> <x2> <y2> <x3> <y3>

where all coordinates are normalized by the frame width and height.

Usage

Download from Hugging Face

from huggingface_hub import snapshot_download

repo_dir = snapshot_download(
    repo_id="YOUR_USERNAME_OR_ORG/MMOT",
    repo_type="dataset",
)

print(repo_dir)

Replace YOUR_USERNAME_OR_ORG/MMOT with the actual Hugging Face dataset repository ID.

Convert Hugging Face Archives to the Standard Layout

python dataset/huggingface_tar_to_standard.py --root /path/to/MMOT_DATASET

This extracts each sequence archive into <split>/npy/<seq_name>/ and merges per-frame .txt files into <split>/mot/<seq_name>.txt.

Example: Load a Multispectral Frame

from pathlib import Path
import numpy as np

root = Path("MMOT_DATASET")
seq = "data23-2"
frame_path = root / "train" / "npy" / seq / "000001.npy"

frame = np.load(frame_path)
print(frame.shape)  # Usually H x W x 8, depending on the saved array layout

Example: Read MOT-OBB Annotations with Python

from pathlib import Path
import pandas as pd

root = Path("MMOT_DATASET")
seq = "data23-2"
ann_path = root / "train" / "mot" / f"{seq}.txt"

columns = [
    "frame", "id",
    "x0", "y0", "x1", "y1", "x2", "y2", "x3", "y3",
    "score_or_flag", "class", "truncation_or_extra_flag",
]

ann = pd.read_csv(ann_path, header=None, names=columns)
print(ann.head())
print(ann["id"].nunique(), "unique object tracks")

Example: Get Annotations for One Frame

frame_idx = 1
frame_ann = ann[ann["frame"] == frame_idx]

corners = frame_ann[["x0", "y0", "x1", "y1", "x2", "y2", "x3", "y3"]].to_numpy()
classes = frame_ann["class"].astype(int).to_numpy()
ids = frame_ann["id"].astype(int).to_numpy()

print(corners.shape, classes.shape, ids.shape)

Example: Create Pseudo-RGB from Multispectral Data

The MMOT paper evaluates RGB baselines by synthesizing pseudo-RGB images from MSI bands 5, 3, and 2. Adjust indexing according to your array convention, because Python uses zero-based indexing.

import numpy as np

frame = np.load("MMOT_DATASET/train/npy/data23-2/000001.npy")

# If frame is H x W x 8 and the paper's band numbers are treated as 1-based:
pseudo_rgb = frame[:, :, [4, 2, 1]]

# Optional normalization for visualization only
pseudo_rgb = pseudo_rgb.astype("float32")
pseudo_rgb = (pseudo_rgb - pseudo_rgb.min()) / (pseudo_rgb.max() - pseudo_rgb.min() + 1e-6)

Evaluation

MMOT is evaluated with MOT-style metrics, including:

• HOTA
• MOTA
• IDF1
• DetA
• AssA
• CLEAR MOT metrics

The official repository integrates TrackEval for MMOT evaluation. The paper reports two aggregation protocols for the multi-class setting:

• Class-averaged evaluation: average metrics across classes.
• Detection-averaged evaluation: aggregate detections across classes before computing metrics.

The paper evaluates both RGB and MSI input settings:

• RGB setting: pseudo-RGB images are synthesized by selecting bands 5, 3, and 2 from the MSI cube.
• MSI setting: all 8 spectral channels are used.

For official benchmark reporting, use the MMOT authors' evaluation code and protocol. For local experiments:

• Keep training and test sequences separate.
• Do not tune hyperparameters on test-set ground truth.
• Report whether the tracker uses RGB, pseudo-RGB, or full MSI input.
• Report whether OBBs are evaluated as corner polygons, rotated rectangles, or converted axis-aligned boxes.
• Use the expected sequence-local identity IDs and per-sequence output files.

Dataset Creation

Source Data

MMOT was collected using a UAV equipped with a downward-facing multispectral camera. The camera captures 8 spectral bands spanning the visible to near-infrared range. The data were collected at dynamic flight altitudes from 80 m to 200 m, under conditions including clear skies, cloudy days, and dense fog.

The benchmark covers diverse scenes such as urban streets, rural fields, traffic intersections, transit hubs, playgrounds, and sports courts. Frames were registered across spectral channels and cropped to 1200 × 900 pixels.

Annotation Guidelines

MMOT uses oriented bounding boxes because arbitrary object orientation is common in aerial views. The annotation process assigns a unique identity to each object across frames and uses a multi-stage annotation pipeline.

The paper describes the following annotation principles:

• Exhaustively annotate instances in the predefined categories.
• Use spectral channels to assist when targets are difficult to distinguish in pseudo-color views.
• Validate ambiguous targets using temporal context across the full sequence.
• Label the full object extent, including under occlusion, truncation, or motion blur.
• Maintain consistent identity IDs over time.

The annotation workflow includes initial box placement, box refinement, identity assignment, identity correction, and expert-level cross-validation.

Intended Use

MMOT is intended for academic research and benchmarking in:

• Multispectral multi-object tracking
• Drone-based MOT
• Oriented and rotation-aware tracking
• Multi-class aerial tracking
• Robust association under small-object, dense, occluded, or fast-motion conditions
• Multispectral representation learning

Out-of-Scope Use

This dataset should not be used for:

• Surveillance deployments targeting private individuals without appropriate legal basis and safeguards
• Biometric identification or person identification outside the dataset's research context
• Commercial use that violates the dataset license
• Redistribution, modification, or derivative release that violates the original MMOT terms
• Claims of official benchmark performance without following the official evaluation protocol

Limitations and Biases

MMOT focuses on UAV-based multispectral tracking and may not generalize to ground-level videos, fixed surveillance cameras, non-UAV sensors, different camera spectral responses, or domains outside the covered scene types.

Because the data are collected from aerial views, many objects are small and appearance cues can be weak. Models trained only on MMOT may overfit to the dataset's specific flight altitudes, object categories, geographic environments, weather conditions, spectral bands, and annotation conventions.

The dataset is valuable for research on multispectral tracking, but high-quality OBB annotation is labor-intensive. The paper identifies scalable annotation and unsupervised learning as future directions.

License and Terms

The MMOT dataset is released under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0) according to the official repository and Hugging Face listing.

The dataset is intended for academic research. Users must attribute the original source and should not modify or redistribute the dataset without permission. Please consult the official MMOT repository, dataset page, and license before redistributing, modifying, or using the dataset commercially.

Code in the official repository may contain multiple submodules with their own licenses. Check the license file or terms in each subdirectory before reusing code.

Citation

If you use MMOT, please cite the original paper:

@inproceedings{li2025mmot,
  title     = {MMOT: The First Challenging Benchmark for Drone-based Multispectral Multi-Object Tracking},
  author    = {Li, Tianhao and Xu, Tingfa and Wang, Ying and Qin, Haolin and Lin, Xu and Li, Jianan},
  booktitle = {NeurIPS 2025 Datasets and Benchmarks Track},
  year      = {2025},
  url       = {https://arxiv.org/abs/2510.12565}
}

Acknowledgements

MMOT was created by researchers from Beijing Institute of Technology and Beijing Institute of Technology Chongqing Innovation Center. Please refer to the original paper and official repository for complete author information, benchmark details, code, pretrained models, and updates.