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Updated: Jan 9, 2026

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

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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
PubMed
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
12:39

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

Published on: January 18, 2020

8.1K
  • 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.