基于前景区域生成对抗网络的视频异常行为检测研究

邝永年; 王丰

doi:10.12052/gdutxb.220179

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基于前景区域生成对抗网络的视频异常行为检测研究

计算机科学与技术 | 更新时间：2026-05-27

- 基于前景区域生成对抗网络的视频异常行为检测研究
- Video Frame Anomaly Behavior Detection Based on Foreground Area Generative Adversarial Networks
- 广东工业大学学报 2024年41卷第1期页码：63-68, 92
- 作者机构：
  
  广东工业大学信息工程学院,广东,广州,510006
- 作者简介：
  
  邝永年(1995–)，男，硕士研究生，主要研究方向为视频异常检测
  王丰(1987–)，男，副教授，博士，主要研究方向为通信信号处理、移动边缘计算与机器视觉，E-mail：fengwang13@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61901124);广东省自然科学基金资助项目(2021A1515012305);广州市基础研究计划项目(202102020856)
- DOI：10.12052/gdutxb.220179
  中图分类号： TP391
- 纸质出版：2024
- 稿件说明：
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邝永年, 王丰. 基于前景区域生成对抗网络的视频异常行为检测研究[J]. 广东工业大学学报, 2024, 41(1): 63-68, 92.

Kuang Yong-nian, Wang Feng. Video Frame Anomaly Behavior Detection Based on Foreground Area Generative Adversarial Networks[J]. 2024, 41(1): 63-68, 92.
邝永年, 王丰. 基于前景区域生成对抗网络的视频异常行为检测研究[J]. 广东工业大学学报, 2024, 41(1): 63-68, 92. DOI： 10.12052/gdutxb.220179.

Kuang Yong-nian, Wang Feng. Video Frame Anomaly Behavior Detection Based on Foreground Area Generative Adversarial Networks[J]. 2024, 41(1): 63-68, 92. DOI： 10.12052/gdutxb.220179.

摘要

为提高视频异常行为检测的准确率，本文提出了一种基于前景区域生成对抗网络的改进方法。通过提取实际视频帧的前景和背景掩码，确定生成对抗网络输出视频帧的待检测前景区域。针对待检测前景区域，应用前景区域峰值信噪比准则，计算异常行为检测得分，完成视频异常行为检测。实验结果表明，本文的检测方法在Avenue数据集、UCSD-Ped1数据集、UCSD-Ped2数据集上均能有效提高视频异常行为检测准确率，并能降低检测运行时间。

Abstract

In order to improve the accuracy of video anomaly behavior detection

a new detection method based on generative adversarial networks for video foreground areas is proposed. First

the foreground and background masks of the ground truth video frame are extracted

to determine the foreground areas of the output video frames from generative adversarial networks. For the foreground areas under consideration

the foreground area peak signal-to-noise ratio (F-PSNR) is used to calculate the detection score of anomaly behaviors. The experimental results show that the proposed method can effectively improve the detection accuracy of video anomaly behaviors with a reduced detection time for the Avenue dataset

UCSD-Ped1 dataset and UCSD-Ped2 dataset.

关键词

Keywords

references

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