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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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A Comprehensive Review on Temporal-Action Proposal Generation.

Sorn Sooksatra1, Sitapa Watcharapinchai1

  • 1National Electronic and Computer Technology Center, National Science and Technology Development Agency, Pathum Thani 12120, Thailand.

Journal of Imaging
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

This review synthesizes research on temporal-action proposal generation (TAPG), a key step for action localization in videos. Current methods achieve 60-78% average recall, highlighting areas for future research.

Keywords:
proposal evaluation networktemporal-action proposal generationtime-series analysisvideo descriptor

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Temporal-action proposal generation (TAPG) is a critical pre-processing step for temporal-action localization in untrimmed videos.
  • Recent research emphasizes anchor- and boundary-based methods for generating action proposals.
  • Interest in TAPG has grown significantly, impacting overall action localization performance.

Purpose of the Study:

  • To provide a comprehensive review of temporal-action proposal generation (TAPG) techniques.
  • To analyze network architectures, empirical results, and pre-processing steps for TAPG.
  • To identify limitations and future research directions in the field.

Main Methods:

  • A systematic literature review of 71 studies from 2012-2022 on temporal-action proposal generation.
  • Selection of studies based on contributions and evaluation criteria from major databases.
  • Summarization and analysis of methodologies and contributions in tabular form.

Main Results:

  • TAPG performance, measured by average recall, ranges from 60% to 78% across two benchmarks.
  • Analysis of state-of-the-art research reveals current limitations and challenges in action proposal generation.
  • Contributions and methodologies of various TAPG approaches are categorized and analyzed.

Conclusions:

  • The review consolidates current knowledge on TAPG, offering insights into network architectures and performance.
  • Identified limitations point towards specific areas for future research and development in action proposal generation.
  • The findings provide a foundation for advancing temporal-action localization techniques.