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Multilevel LSTM for Action Recognition Based on Skeleton Sequence
CHEN Yan-Ru 1, PAN Hua-Wei 2 *
1. College of Information Science and Engineering, Hunan University, Changsha 410000
2. College of Information Science and Engineering, Hunan University, Changsha 410000
*Correspondence author
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Funding: none
Opened online:24 April 2019
Accepted by: none
Citation: CHEN Yan-Ru, PAN Hua-Wei.Multilevel LSTM for Action Recognition Based on Skeleton Sequence[OL]. [24 April 2019] http://en.paper.edu.cn/en_releasepaper/content/4748531
 
 
Skeleton-based human action recognition has a broad range of applications in human-computer interaction and intelligent monitoring, and human behavior can be represented by the trajectory of the skeleton joint. Long-term short-term memory (LSTM) networks exhibit outstanding performance in 3D human action recognition because they are capable of modeling dynamics and dependencies in sequential data. In this paper, we propose a skeleton-based multilevel LSTM network for action recognition. First, the data for each joint and parent joint is used as input to a fine-grained subnet based on the action link between them. Then the features of the upper body joint are merged into the upper body subnet, the features of the lower body are merged into the lower body subnet, and finally the features of the two subnets are structured and fused to achieve higher recognition accuracy. Experimental results on the public data set NTU RGB+D demonstrate the effectiveness of the proposed network.
Keywords:Pattern recognition; Action recognition; LSTM; Skeleton sequence
 
 
 

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