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Updated: Sep 4, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Online action proposal generation using spatio-temporal attention network.

Kanchan Keisham1, Amin Jalali2, Minho Lee3

  • 1Graduate School of Artificial Intelligence, Kyungpook National University, Daegu, 41566, South Korea.

Neural Networks : the Official Journal of the International Neural Network Society
|July 14, 2022
PubMed
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This study introduces a novel spatio-temporal attention network for online action proposal generation. The method efficiently creates precise action boundaries in real-time video analysis, overcoming limitations of existing offline approaches.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Real-time applications like surveillance and autonomous driving require online localization and recognition of human activities.
  • Existing offline temporal action proposal generation methods suffer from high computational costs and slow processing speeds due to redundant proposal regions.
  • These limitations make current methods unsuitable for time-sensitive online tasks.

Purpose of the Study:

  • To propose a novel spatio-temporal attention network for efficient online action proposal generation.
  • To address the limitations of existing offline methods in real-time video analysis.
  • To generate precise temporal action proposals with low computational cost and high processing speed.

Main Methods:

Keywords:
Action detectionSpatial attentionTemporal action proposalTemporal attention

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  • A novel spatio-temporal attention network is proposed, integrating spatial and temporal context for online action proposal generation.
  • A windowed spatial attention module captures inter-spatial relationships within incoming frames, creating robust clip-level features and handling noisy data.
  • A temporal attention module models long inter-frame relationships by capturing relevant temporal dynamics, complementing spatial information.

    • Main Results:

    • The proposed network generates more relevant online temporal action proposals by incorporating inter-dependencies between spatial and temporal contexts.
    • The model produces fewer discriminative temporal action proposals, enhancing efficiency.
    • The approach achieves precise action boundary generation at specific time instances.

    Conclusions:

    • The novel spatio-temporal attention network is effective for online action proposal generation, suitable for real-time applications.
    • The method overcomes the drawbacks of offline approaches, offering improved speed and reduced computational load.
    • This research contributes to more efficient and accurate human activity analysis in dynamic video streams.