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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

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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:

Accuracy CNN Deep learning Feature-based approaches Human 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.