利用三维卷积和长短时记忆模块的高效时空特征融合进行视频异常检测

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-06-19 DOI:10.1002/aisy.202300706
Sareer Ul Amin, Bumsoo Kim, Yonghoon Jung, Sanghyun Seo, Sangoh Park
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引用次数: 0

摘要

监控摄像机会产生大量视频数据,由于异常事件并不常见,这给分析人员带来了挑战。为解决这一问题,智能监控系统利用人工智能和计算机视觉来自动检测异常情况。本研究提出了一种结合三维卷积和长短期记忆(LSTM)模块的创新方法,以捕捉视频数据中的时空特征。值得注意的是,结构化粗级特征融合机制增强了泛化能力,并缓解了梯度消失的问题。与传统的卷积神经网络不同,该方法采用了深度特征堆叠,从而降低了计算复杂度并增强了架构。此外,它还集成了用于下采样的微自动编码器块,消除了 ConvLSTM2D 层的计算负荷,并在上采样期间采用频繁的特征串联块来保留时间信息。在下采样和上采样阶段集成 Conv-LSTM 模块,增强了模型捕捉短期和长期时间特征的能力,从而在保持稳健性能的同时,实现了 42 层网络。实验结果表明,与同类方法相比,误报率明显降低,准确率也有所提高,在 UCSDPed1、UCSDPed2 和 Avenue 数据集上的误报率分别提高了 2.7%、0.6% 和 3.4%。
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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Video Anomaly Detection Utilizing Efficient Spatiotemporal Feature Fusion with 3D Convolutions and Long Short-Term Memory Modules

Surveillance cameras produce vast amounts of video data, posing a challenge for analysts due to the infrequent occurrence of unusual events. To address this, intelligent surveillance systems leverage AI and computer vision to automatically detect anomalies. This study proposes an innovative method combining 3D convolutions and long short-term memory (LSTM) modules to capture spatiotemporal features in video data. Notably, a structured coarse-level feature fusion mechanism enhances generalization and mitigates the issue of vanishing gradients. Unlike traditional convolutional neural networks, the approach employs depth-wise feature stacking, reducing computational complexity and enhancing the architecture. Additionally, it integrates microautoencoder blocks for downsampling, eliminates the computational load of ConvLSTM2D layers, and employs frequent feature concatenation blocks during upsampling to preserve temporal information. Integrating a Conv-LSTM module at the down- and upsampling stages enhances the model's ability to capture short- and long-term temporal features, resulting in a 42-layer network while maintaining robust performance. Experimental results demonstrate significant reductions in false alarms and improved accuracy compared to contemporary methods, with enhancements of 2.7%, 0.6%, and 3.4% on the UCSDPed1, UCSDPed2, and Avenue datasets, respectively.

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CiteScore
1.30
自引率
0.00%
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审稿时长
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