人类活动识别：基于深度学习的方法综述

IF 1.5 4区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IET Computer Vision Pub Date : 2025-02-01 DOI:10.1049/cvi2.70003

Sanjay Jyoti Dutta, Tossapon Boongoen, Reyer Zwiggelaar

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引用次数: 0

摘要

人类活动识别（HAR）涵盖了从数据流中自动识别人类活动的方法。HAR方法的最终用户涵盖一系列部门，包括卫生、自我保健、娱乐、安全和监测。在本调查中，作者提供了基于深度学习的全面概述，并详细分析了2018年至2023年期间在与HAR相关的各种领域进行的工作，重点是无设备解决方案。它还介绍了所涵盖出版物的分类和分类法以及公开可用数据集的概述。为了完成这一综述，讨论了现有方法的局限性和潜在的未来研究方向。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Human activity recognition: A review of deep learning-based methods

查看原文本刊更多论文

Human activity recognition: A review of deep learning-based methods

Human Activity Recognition (HAR) covers methods for automatically identifying human activities from a stream of data. End-users of HAR methods cover a range of sectors, including health, self-care, amusement, safety and monitoring. In this survey, the authors provide a thorough overview of deep learning based and detailed analysis of work that was performed between 2018 and 2023 in a variety of fields related to HAR with a focus on device-free solutions. It also presents the categorisation and taxonomy of the covered publication and an overview of publicly available datasets. To complete this review, the limitations of existing approaches and potential future research directions are discussed.

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来源期刊

IET Computer Vision 工程技术-工程：电子与电气

CiteScore

3.30

自引率

11.80%

发文量

审稿时长

3.4 months

期刊介绍： IET Computer Vision seeks original research papers in a wide range of areas of computer vision. The vision of the journal is to publish the highest quality research work that is relevant and topical to the field, but not forgetting those works that aim to introduce new horizons and set the agenda for future avenues of research in computer vision. IET Computer Vision welcomes submissions on the following topics: Biologically and perceptually motivated approaches to low level vision (feature detection, etc.); Perceptual grouping and organisation Representation, analysis and matching of 2D and 3D shape Shape-from-X Object recognition Image understanding Learning with visual inputs Motion analysis and object tracking Multiview scene analysis Cognitive approaches in low, mid and high level vision Control in visual systems Colour, reflectance and light Statistical and probabilistic models Face and gesture Surveillance Biometrics and security Robotics Vehicle guidance Automatic model aquisition Medical image analysis and understanding Aerial scene analysis and remote sensing Deep learning models in computer vision Both methodological and applications orientated papers are welcome. Manuscripts submitted are expected to include a detailed and analytical review of the literature and state-of-the-art exposition of the original proposed research and its methodology, its thorough experimental evaluation, and last but not least, comparative evaluation against relevant and state-of-the-art methods. Submissions not abiding by these minimum requirements may be returned to authors without being sent to review. Special Issues Current Call for Papers: Computer Vision for Smart Cameras and Camera Networks - https://digital-library.theiet.org/files/IET_CVI_SC.pdf Computer Vision for the Creative Industries - https://digital-library.theiet.org/files/IET_CVI_CVCI.pdf