广东工业大学学报 ›› 2024, Vol. 41 ›› Issue (01): 63-68,92.doi: 10.12052/gdutxb.220179

• 计算机科学与技术 • 上一篇    下一篇

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   

  1. 广东工业大学 信息工程学院, 广东 广州 510006
  • 收稿日期:2022-12-01 出版日期:2024-01-25 发布日期:2024-02-01
  • 通信作者: 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
  • 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
  • 基金资助:
    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)

Video Frame Anomaly Behavior Detection Based on Foreground Area Generative Adversarial Networks

Kuang Yong-nian, Wang Feng   

  1. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2022-12-01 Online:2024-01-25 Published:2024-02-01

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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。

关键词: 视频异常行为检测, 峰值信噪比, 生成对抗网络, 前景区域

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.

Key words: video anomaly behavior detection, peak signal-to-noise ratio, generative adversarial networks, foreground area

中图分类号: 

  • TP391
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