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A deep neural network model for multi-view human activity recognition.

Prasetia Utama Putra¹, Keisuke Shima², Koji Shimatani³

¹Graduate School of Engineering, Yokohama National University, Yokohama, Kanagawa, Japan.

|January 7, 2022

Summary

This summary is machine-generated.

This study introduces a deep neural network (DNN) model for multi-view human activity recognition (MVHAR) that overcomes data limitations. The model achieves high accuracy on challenging datasets, improving human activity classification.

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Single-view human activity recognition struggles with occlusion.
Deep neural networks (DNNs) show promise for multi-view human activity recognition (MVHAR).
Existing datasets are insufficient for training accurate DNN models for MVHAR.

Purpose of the Study:

To develop a DNN model for accurate human activity classification using multi-view camera inputs.
To address the limitations of current datasets in MVHAR research.

Main Methods:

Proposed a DNN model incorporating transfer learning and shared-weight techniques.
Model architecture includes pre-trained convolutional neural networks (CNNs), attention layers, long short-term memory networks with residual learning (LSTMRes), and Softmax layers.
Trained the model using challenging multi-view human activity recognition datasets.

Main Results:

Achieved high accuracy on the IXMAS dataset (97.27%) and i3DPost dataset (96.87%).
Demonstrated competitive performance in online human activity classification.
The proposed model effectively resolves occlusion issues in human activity recognition.

Conclusions:

The developed DNN model offers a promising solution for accurate MVHAR.
Transfer learning and shared-weight techniques effectively mitigate data scarcity issues.
The model's architecture is suitable for real-time and challenging human activity recognition tasks.