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Spatio-Temporal Scale Coded Bag-of-Words.

Divina Govender1, Jules-Raymond Tapamo1

  • 1School of Engineering, University of KwaZulu-Natal, Durban 4041, South Africa.

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|November 13, 2020
PubMed
Summary

A new spatio-temporal scale coded Bag-of-Words (SC-BoW) improves video action recognition. This efficient method enhances classification power with minimal computational cost, outperforming complex deep learning models.

Keywords:
Bag-of-Wordsaction recognitioncomputational efficiencyreal-time systems

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • The Bag-of-Words (BoW) framework is a standard for action recognition, offering efficient feature representation.
  • Modifications to BoW often increase complexity and reduce efficiency, limiting its practical application.
  • Image-based scale-coded BoW has shown promise in enhancing representation power.

Purpose of the Study:

  • To introduce a novel spatio-temporal scale coded Bag-of-Words (SC-BoW) framework for video-based action recognition.
  • To evaluate the effectiveness of SC-BoW in improving classification accuracy without compromising efficiency.
  • To demonstrate the applicability of SC-BoW in real-time action recognition pipelines.

Main Methods:

  • Extracted multi-scale spatio-temporal features are encoded into BoW representations.
  • Features are partitioned into sub-groups based on their spatial scale of extraction.
  • SC-BoW was evaluated using a general real-time action recognition pipeline and applied to the Dense Trajectory feature set.

Main Results:

  • SC-BoW representations improved action recognition performance by 2-7% across experimental setups.
  • The proposed SC-BoW method demonstrated a low added computational cost.
  • SC-BoW applied to Dense Trajectories outperformed more complex deep learning approaches.

Conclusions:

  • Scale coding is an effective low-cost, low-level encoding scheme for enhancing BoW representations.
  • SC-BoW significantly increases the classification power of standard BoW frameworks.
  • The SC-BoW framework offers a computationally efficient and powerful solution for video action recognition.