The Role of Deep Learning Models in the Detection of Anti-Social Behaviours towards Women in Public Transport from Surveillance Videos: A Scoping Review

IF 1.8 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Safety Pub Date : 2023-12-13 DOI:10.3390/safety9040091
M. Papini, Umair Iqbal, Johan Barthelemy, Christian Ritz
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引用次数: 0

Abstract

Increasing women’s active participation in economic, educational, and social spheres requires ensuring safe public transport environments. This study investigates the potential of machine learning-based models in addressing behaviours impacting the safety perception of women commuters. Specifically, we conduct a comprehensive review of the existing literature concerning the utilisation of deep learning models for identifying anti-social behaviours in public spaces. Employing a scoping review methodology, our study synthesises the current landscape, highlighting both the advantages and challenges associated with the automated detection of such behaviours. Additionally, we assess available video and audio datasets suitable for training detection algorithms in this context. The findings not only shed light on the feasibility of leveraging deep learning for recognising anti-social behaviours but also provide critical insights for researchers, developers, and transport operators. Our work aims to facilitate future studies focused on the development and implementation of deep learning models, enhancing safety for all passengers in public transportation systems.
深度学习模型在从监控视频中检测公共交通中针对女性的反社会行为中的作用:范围审查
要提高女性在经济、教育和社会领域的积极参与程度,就必须确保安全的公共交通环境。本研究探讨了基于机器学习的模型在解决影响女性乘客安全感的行为方面的潜力。具体来说,我们对现有文献中有关利用深度学习模型识别公共场所反社会行为的内容进行了全面回顾。我们的研究采用了范围综述的方法,综合了当前的研究情况,突出了与自动检测此类行为相关的优势和挑战。此外,我们还评估了适合在此背景下训练检测算法的可用视频和音频数据集。研究结果不仅阐明了利用深度学习识别反社会行为的可行性,还为研究人员、开发人员和交通运营商提供了重要见解。我们的工作旨在促进未来以开发和实施深度学习模型为重点的研究,从而提高公共交通系统中所有乘客的安全。
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
Safety
Safety Social Sciences-Safety Research
CiteScore
3.20
自引率
5.30%
发文量
71
审稿时长
7 weeks
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