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Human Action Recognition From Various Data Modalities: A Review.

Zehua Sun, Qiuhong Ke, Hossein Rahmani

IEEE Transactions on Pattern Analysis and Machine Intelligence

|June 14, 2022

Summary

This summary is machine-generated.

This survey reviews deep learning for Human Action Recognition (HAR), covering single and multi-modal approaches. It analyzes various data types like RGB and skeleton for improved human behavior understanding.

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Area of Science:

Computer Vision
Machine Learning
Human-Computer Interaction

Background:

Human Action Recognition (HAR) is crucial for understanding behavior, with broad applications.
Diverse data modalities (RGB, skeleton, audio, WiFi) offer distinct information for HAR.
Increasing attention on deep learning methods for HAR necessitates a comprehensive review.

Approach:

This article surveys deep learning methods for HAR based on input data modality.
Reviews mainstream single and multi-modal deep learning approaches.
Examines fusion-based and co-learning frameworks for multi-modal HAR.

Key Points:

Deep learning excels in HAR across various single data modalities.
Multi-modal HAR leverages fusion and co-learning for enhanced performance.
Comparative results on benchmark datasets provide performance insights.

Conclusions:

Deep learning offers powerful tools for Human Action Recognition.
The choice of data modality significantly impacts HAR performance.
Future research directions focus on optimizing multi-modal strategies and novel architectures.