.png){kind=icon}
返回
Human Action Recognition Based on Transfer Learning Approach
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3086668.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Human action recognition techniques have gained significant attention among next-generation technologies due to their specific features and high capability to inspect video sequences to understand human actions. As a result, many fields have benefited from human action recognition techniques. Deep learning techniques played a primary role in many approaches to human action recognition. The new era of learning is spreading by transfer learning. Accordingly, this study's main objective is to propose a framework with three main phases for human action recognition. The phases are pre-training, preprocessing, and recognition. This framework presents a set of novel techniques that are three-fold as follows, (i) in the pre-training phase, a standard convolutional neural network is trained on a generic dataset to adjust weights; (ii) to perform the recognition process, this pre-trained model is then applied to the target dataset; and (iii) the recognition phase exploits convolutional neural network and long short-term memory to apply five different architectures. Three architectures are stand-alone and single-stream, while the other two are combinations between the first three in two-stream style. Experimental results show that the first three architectures recorded accuracies of 83.24%, 90.72%, and 90.85%, respectively. The last two architectures achieved accuracies of 93.48% and 94.87%, respectively. Moreover, The recorded results outperform other state-of-the-art models in the same field.
Keyword:
Transfer learning
Feature extraction
Three-dimensional displays
Solid modeling
Deep learning
Computer architecture
Training
Convolutional neural network (CNN)
human action recognition (HAR)
long short-term memory (LSTM)
spatiotemporal info
transfer learning (TL)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Desempenho de crianças do ensino fundamental na solução de problemas aritméticos<A NAME="top"></A>问题的基本解决方案 
OAAI
OAAI分享收藏Implementation of a Virtual Training Simulator Based on 360°Multi-View Human Action Recognition.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容