基于深度学习的监控视频中的人群动态分析和行为识别

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Anum Ilyas, Narmeen Bawany
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引用次数: 0

摘要

视频监控被广泛应用于各个领域,如执法、COVID-19 隔离监控以及分析人群中的潜在威胁(如快闪或暴力)。监控设备每天产生的大量数据蕴含着巨大的潜力,但需要进行有效分析才能提取价值。检测可能导致混乱和伤亡的异常人群行为在视频监控中尤其具有挑战性,因为它需要大量人力,而且容易出错。为了应对这些挑战,本研究在两个关键领域做出了贡献:首先,创建了一个多样化、具有代表性的视频数据集,准确反映了现实世界中八个不同类别的人群动态;其次,开发了一个可靠的框架 "CRAB-NET",用于自动行为识别。通过使用卷积长短期记忆网络(ConvLSTM)和长期递归卷积网络(LRCN)进行广泛的实验和评估,验证了所提出的方法在对监控视频中观察到的行为进行准确分类方面的有效性。所采用的模型对庆祝人群的准确率达到 99.46%,对正式人群的准确率达到 99.98%,对暴力人群的准确率达到 96.69%。LRCN 对综合数据集的准确率达到了 97.20%,这突显了它在人群行为分析方面的革命性潜力。它能确保更安全的人群聚集和更有效的安全干预。将像 "CRAB-NET "这样的人工智能人群行为识别技术纳入安保措施,不仅能保障公众集会的安全,还能为积极主动的活动管理和预测性安全策略铺平道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Crowd dynamics analysis and behavior recognition in surveillance videos based on deep learning

Crowd dynamics analysis and behavior recognition in surveillance videos based on deep learning

Video surveillance is widely adopted across various sectors for purposes such as law enforcement, COVID-19 isolation monitoring, and analyzing crowds for potential threats like flash mobs or violence. The vast amount of data generated daily from surveillance devices holds significant potential but requires effective analysis to extract value. Detecting anomalous crowd behavior, which can lead to chaos and casualties, is particularly challenging in video surveillance due to its labor-intensive nature and susceptibility to errors. To address these challenges, this research contributes in two key areas: first, by creating a diverse and representative video dataset that accurately reflects real-world crowd dynamics across eight different categories; second, by developing a reliable framework, ‘CRAB-NET,’ for automated behavior recognition. Extensive experimentation and evaluation, using Convolutional Long Short-Term Memory networks (ConvLSTM) and Long-Term Recurrent Convolutional Networks (LRCN), validated the effectiveness of the proposed approach in accurately categorizing behaviors observed in surveillance videos. The employed models were able to achieve the accuracy score of 99.46% for celebratory crowd, 99.98% for formal crowd and 96.69% for violent crowd. The demonstrated accuracy of 97.20% for comprehensive dataset achieved by the LRCN underscores its potential to revolutionize crowd behavior analysis. It ensures safer mass gatherings and more effective security interventions. Incorporating AI-powered crowd behavior recognition like ‘CRAB-NET’ into security measures not only safeguards public gatherings but also paves the way for proactive event management and predictive safety strategies.

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来源期刊
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
期刊介绍: Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed. Specific areas of interest include: - Multimedia Tools: - Multimedia Applications: - Prototype multimedia systems and platforms
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