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Multiple-target tracking in human and machine vision.

Shiva Kamkar1,2, 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
PubMed
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.