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Updated: Dec 24, 2025

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

Multiple-target tracking in human and machine vision.

Shiva Kamkar^1,2, Fatemeh Ghezloo², Hamid Abrishami Moghaddam¹

¹Machine Vision and Medical Image Processing Laboratory, Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran.

Plos Computational Biology

|April 10, 2020

Summary

This summary is machine-generated.

Humans excel at multiple-target tracking (MTT), a skill computer vision algorithms struggle to replicate. This review explores neuroscience and AI approaches, highlighting their complementary potential for advanced tracking systems.

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Area of Science:

Cognitive Neuroscience
Computer Vision
Artificial Intelligence

Background:

Human visual perception enables simultaneous tracking of multiple objects during daily activities.
Existing computer vision algorithms for multiple-target tracking (MTT) are applied in surveillance, sports analysis, and human-computer interaction.
Despite advancements in AI, human MTT capabilities remain largely unimitated in computational models.

Purpose of the Study:

To review multiple-target tracking (MTT) research in neuroscience.
To examine biologically inspired MTT methods in computer vision.
To discuss the complementary relationship between human MTT and AI approaches.

Main Methods:

Literature review of neuroscience studies on human multiple-target tracking.
Survey of artificial intelligence algorithms for automated multiple-target tracking.
Comparative analysis of human and computational MTT mechanisms.

Main Results:

Neuroscience reveals complex behavioral and neural mechanisms underlying human MTT.
Computer vision offers various algorithms for automated MTT with applications in diverse fields.
A significant gap exists between human MTT proficiency and current AI imitation.

Conclusions:

Neuroscience and computer vision offer complementary perspectives on multiple-target tracking.
Integrating insights from human cognition could enhance artificial intelligence MTT algorithms.
Further research can bridge the gap between biological and artificial MTT systems.