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HARNet in deep learning approach-a systematic survey.

Neelam Sanjeev Kumar1, G Deepika2, V Goutham3

  • 1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani, Chennai, Tamil Nadu, 600026, India.

Scientific Reports
|April 10, 2024
PubMed
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This survey explores human action recognition (HAR) using deep learning and computer vision. It introduces HARNet, a novel architecture, and analyzes methods like VideoMAE v2 for improved performance.

Area of Science:

  • Computer Vision
  • Deep Learning
  • Artificial Intelligence

Background:

  • Human Action Recognition (HAR) has evolved from handcrafted features to deep learning.
  • Large-scale datasets are crucial for advancing HAR methodologies.
  • Existing research paradigms in HAR include temporal modeling and spatial feature extraction.

Purpose of the Study:

  • To provide a comprehensive overview of HAR methodologies at the intersection of deep learning and computer vision.
  • To classify research paradigms, highlighting their respective advantages and disadvantages.
  • To introduce HARNet, a novel Multi-Model Deep Learning architecture.

Main Methods:

  • Review of handcrafted feature-based and end-to-end deep learning approaches in HAR.
  • Classification of research paradigms based on temporal modeling and spatial features.
Keywords:
AccuracyCNNDeep learningFeature-based approachesHuman action recognition (HAR)

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  • Development and presentation of HARNet, integrating Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and attention mechanisms.
  • Case study utilizing the VideoMAE v2 method for practical implementation analysis.

    • Main Results:

    • A proposed taxonomy that illuminates the merits and drawbacks of different HAR research paradigms.
    • HARNet demonstrates improved accuracy and robustness through the integration of diverse neural network architectures and attention mechanisms.
    • The VideoMAE v2 case study provides insights into practical implementation challenges and solutions in HAR.

    Conclusions:

    • The survey offers a valuable resource for researchers and practitioners seeking to understand the latest advancements in HAR.
    • HARNet represents a significant step forward in developing more accurate and robust human action recognition systems.
    • The study emphasizes the importance of integrating various deep learning techniques and leveraging large datasets for future HAR research.