A new approach of anomaly detection in shopping center surveillance videos for theft prevention based on RLCNN model.

IF 3.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
PeerJ Computer Science Pub Date : 2025-06-18 eCollection Date: 2025-01-01 DOI:10.7717/peerj-cs.2944
Muhammad Sajid, Ali Haider Khan, Kaleem Razzaq Malik, Javed Ali Khan, Ayed Alwadain
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引用次数: 0

Abstract

The amount of video data produced daily by today's surveillance systems is enormous, making analysis difficult for computer vision specialists. It is challenging to continuously search these massive video streams for unexpected accidents because they occur seldom and have little chance of being observed. Contrarily, deep learning-based anomaly detection decreases the need for human labor and has comparably trustworthy decision-making capabilities, hence promoting public safety. In this article, we introduce a system for efficient anomaly detection that can function in surveillance networks with a modest level of complexity. The proposed method starts by obtaining spatiotemporal features from a group of frames. The multi-layer extended short-term memory model can precisely identify continuing unusual activity in complicated video scenarios of a busy shopping mall once we transmit the in-depth features extracted. We conducted in-depth tests on numerous benchmark datasets for anomaly detection to confirm the proposed framework's functionality in challenging surveillance scenarios. Compared to state-of-the-art techniques, our datasets, UCF50, UCF101, UCFYouTube, and UCFCustomized, provided better training and increased accuracy. Our model was trained for more classes than usual, and when the proposed model, RLCNN, was tested for those classes, the results were encouraging. All of our datasets worked admirably. However, when we used the UCFCustomized and UCFYouTube datasets compared to other UCF datasets, we achieved greater accuracy of 96 and 97, respectively.

基于RLCNN模型的购物中心监控视频防盗异常检测新方法。
如今的监控系统每天产生的视频数据量是巨大的,这使得计算机视觉专家很难进行分析。持续搜索这些庞大的视频流以寻找意外事故是一项挑战,因为它们很少发生,而且几乎没有被观察到的机会。相反,基于深度学习的异常检测减少了对人力的需求,并具有相对可靠的决策能力,从而促进了公共安全。在本文中,我们介绍了一个有效的异常检测系统,该系统可以在具有适度复杂性的监视网络中发挥作用。该方法首先从一组帧中获取时空特征。多层扩展短期记忆模型将提取的深度特征传输到繁忙的购物中心复杂视频场景中,可以准确识别出持续的异常活动。我们对许多基准数据集进行了深入的异常检测测试,以确认所提出的框架在具有挑战性的监控场景中的功能。与最先进的技术相比,我们的数据集,UCF50, UCF101, UCFYouTube和UCFCustomized,提供了更好的训练和更高的准确性。我们的模型接受了比平时更多的类的训练,当我们提出的RLCNN模型针对这些类进行测试时,结果令人鼓舞。我们所有的数据集都工作得很好。然而,当我们使用UCFCustomized和UCFYouTube数据集与其他UCF数据集相比时,我们分别达到了96和97的更高精度。
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
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
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