Aung Sai Thu Ya, Kusakunniran Worapan
Faculty of Information and Communication Technology, Mahidol University, Salaya, Nakhon Pathom, Thailand.
PeerJ Comput Sci. 2024 Nov 22;10:e2456. doi: 10.7717/peerj-cs.2456. eCollection 2024.
Despite the growing worries expressed by privacy supporters about the extensive adoption of gait biometrics, research in this field has been moving forward swiftly. Deep learning, a powerful technology that enables computers to learn from data, has found its way into criminal investigations involving gait. In this survey, the literature of gait analysis concerning criminal investigation is discussed with a comprehensive overview of developments in gait analysis with deep neural networks. Firstly, terminologies and factors regarding human gait with scenarios related to crime are discussed. Subsequently, the areas and domains corresponding to criminal investigation that can be tackled by gait analysis are discussed. Also, deep learning methods for gait analysis and how they can be applied in criminal investigations are presented. Then, gait analysis techniques and approaches using deep learning methods including currently available datasets are mentioned. Moreover, crime-related video datasets are presented with literature on deep learning-based anomaly detection with gait human poses. Finally, challenges regarding gait analysis in criminal investigations are presented with open research issues.
尽管隐私支持者对步态生物识别技术的广泛应用日益担忧，但该领域的研究仍在迅速推进。深度学习作为一种强大的技术，能够使计算机从数据中学习，已在涉及步态的刑事调查中得到应用。在本次综述中，我们将讨论与刑事调查相关的步态分析文献，并全面概述深度神经网络在步态分析中的发展。首先，我们将讨论与犯罪场景相关的人类步态的术语和因素。随后，我们将讨论步态分析可解决的与刑事调查对应的领域和范畴。此外，还将介绍用于步态分析的深度学习方法以及它们如何应用于刑事调查。然后，将提及使用深度学习方法的步态分析技术和方法，包括当前可用的数据集。此外，还将介绍与犯罪相关的视频数据集以及基于深度学习的步态人体姿态异常检测的文献。最后，将介绍刑事调查中步态分析面临的挑战以及开放的研究问题。
