3D Behavior Recognition Based on Multi-Modal Deep Space-Time Learning

3D Behavior Recognition Based on Multi-Modal Deep Space-Time Learning

This paper proposes a dual-stream 3D space-time convolutional neural network action recognition framework. The original depth map sequence data is set as the input in order to study the global space-time characteristics of each action category. The high correlation within the human action itself is...

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Bibliographic Details
Main Authors: Chong Zhao, Minglin Chen, Jinhao Zhao, Qicong Wang, Yehu Shen
Format: Article
Language:English
Published: MDPI AG 2019-02-01
Series:Applied Sciences
Subjects:
space-time characteristics
feature fusion
3D skeleton
Online Access:https://www.mdpi.com/2076-3417/9/4/716

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