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Updated: Sep 3, 2025
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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
Published on: January 23, 2017
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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
Summary
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.
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.
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