Seq2seq model for human action recognition based on skeleton and two-layer bidirectional LSTM

IF 1.8 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Ambient Intelligence and Smart Environments Pub Date : 2023-01-30 DOI:10.3233/ais-220125

Shouke Wei, Jindong Zhao, Junhuai Li, M. Yuan

引用次数: 1

Abstract

Human action recognition (HAR) plays an important role in social interaction in various fields. This study proposes a light-weight skeleton and two-layer bidirectional LSTM-based Seq2Seq model (SB2_Seq2Seq) for HAR to trade off recognition accuracy, users’ privacy and computer resource usage. An experiment was conducted to compare the proposed SB2_Seq2Seq with other skeleton-based Seq2Seq models and non-skeleton RGB video frame-based LSTM, CNN and seq2seq models. The UCF50 dataset was used for model evaluation, where 60%, 20% and 20% for model training, validation and testing, respectively. The experimental results show that the proposed model achieves 93.54% accuracy with 0.0214 Mean Square Error (MSE), suggesting that the proposed model outperforms all the other models. Besides, it also shows that the proposed model achieves state-of-the-art accuracy compared with state-of-the-arts methods in literature.

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基于骨架和双层双向LSTM的人类动作识别Seq2seq模型

人类行为识别（HAR）在各个领域的社会互动中发挥着重要作用。本研究为HAR提出了一种基于轻量级骨架和两层双向LSTM的Seq2Seq模型（SB2_Seq2Seq），以权衡识别准确性、用户隐私和计算机资源使用。进行了一项实验，以将所提出的SB2_Seq2Seq与其他基于骨架的Seq2Seq模型以及基于非骨架RGB视频帧的LSTM、CNN和Seq2Seq模型进行比较。UCF50数据集用于模型评估，其中60%、20%和20%分别用于模型训练、验证和测试。实验结果表明，该模型的准确率为93.54%，均方误差为0.0214，优于其他模型。此外，它还表明，与文献中最先进的方法相比，所提出的模型达到了最先进的精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Ambient Intelligence and Smart Environments COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS

CiteScore

4.30

自引率

17.60%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of Ambient Intelligence and Smart Environments (JAISE) serves as a forum to discuss the latest developments on Ambient Intelligence (AmI) and Smart Environments (SmE). Given the multi-disciplinary nature of the areas involved, the journal aims to promote participation from several different communities covering topics ranging from enabling technologies such as multi-modal sensing and vision processing, to algorithmic aspects in interpretive and reasoning domains, to application-oriented efforts in human-centered services, as well as contributions from the fields of robotics, networking, HCI, mobile, collaborative and pervasive computing. This diversity stems from the fact that smart environments can be defined with a variety of different characteristics based on the applications they serve, their interaction models with humans, the practical system design aspects, as well as the multi-faceted conceptual and algorithmic considerations that would enable them to operate seamlessly and unobtrusively. The Journal of Ambient Intelligence and Smart Environments will focus on both the technical and application aspects of these.