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Updated: Jan 9, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

M3OT: A Multi-Drone Multi-Modality dataset for Multi-Object Tracking.

Zhihao Nie¹, Luyi Xue¹, Zhenyu Fang²

¹School of Software, Northwestern Polytechnical University, Xi'an, 710072, China.

Scientific Data

|December 8, 2025

Summary

This summary is machine-generated.

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The M3OT dataset offers challenging aerial vehicle detection and tracking using multi-drone, multi-modality data. This benchmark, featuring small objects from high altitudes, pushes the limits of current tracking algorithms.

Area of Science:

Computer Vision
Robotics
Remote Sensing

Background:

Object detection and tracking from Unmanned Aerial Vehicles (UAVs) are crucial for various applications.
Existing datasets often lack multi-modality (RGB and infrared thermal) and high-altitude perspectives, limiting algorithm development for small object detection.
Multi-drone data acquisition presents unique challenges in synchronization and data fusion.

Purpose of the Study:

To introduce the M3OT dataset, a novel multi-drone, multi-modality dataset for vehicle detection and tracking.
To provide a challenging benchmark for evaluating multiple object tracking algorithms, particularly for small objects in high-altitude aerial imagery.
To facilitate research and development in UAV-based surveillance and reconnaissance systems.

Main Methods:

Related Experiment Videos

Last Updated: Jan 9, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Acquisition of aerial imagery using two Unmanned Aerial Vehicles (UAVs) at altitudes of 100-120m.
Collection of synchronized RGB and infrared thermal (IR) video data across diverse environments (suburban, urban) and lighting conditions (daytime, dusk, night).
Annotation of over 220,000 bounding boxes for vehicles, focusing on small objects, across 21,580 frames from 8-hour video footage.

Main Results:

The M3OT dataset comprises 10,790 paired RGB-IR images, presenting a significant challenge due to the prevalence of small objects.
Evaluation of state-of-the-art multiple object tracking algorithms on M3OT indicates substantial performance limitations.
The dataset serves as a rigorous benchmark, highlighting the need for advanced algorithms for robust aerial vehicle tracking.

Conclusions:

The M3OT dataset is the first multi-drone, multi-modality benchmark specifically designed for multiple object tracking challenges.
The dataset's unique characteristics, including high-altitude acquisition and small object focus, push the boundaries of current detection and tracking capabilities.
M3OT is expected to significantly advance research in UAV-based vehicle detection and tracking applications.