Video Frame Anomaly Behavior Detection Method Based on Masked Convolution and External Attention

Kuang Yongnian; Wang Feng; Ding Ke

doi:10.12052/gdutxb.230202

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Video Frame Anomaly Behavior Detection Method Based on Masked Convolution and External Attention

更新时间：2026-05-27

- Video Frame Anomaly Behavior Detection Method Based on Masked Convolution and External Attention
- Vol. 42, Issue 3, Pages: 130-136(2025)
- 作者机构：
  
  1. 广东工业大学信息工程学院,广东,广州,510006
  2. 佛山显扬科技有限公司,广东,佛山,528200
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.230202
  CLC： TP391
- Published：2025
- 稿件说明：
移动端阅览
邝永年, 王丰, 丁克. 基于掩蔽卷积及外部注意力的视频异常行为检测算法[J]. 广东工业大学学报, 2025, 42(3): 130–136.

Kuang Yongnian, Wang Feng, Ding Ke. Video Frame Anomaly Behavior Detection Method Based on Masked Convolution and External Attention[J]. 2025, 42(3): 130-136.
邝永年, 王丰, 丁克. 基于掩蔽卷积及外部注意力的视频异常行为检测算法[J]. 广东工业大学学报, 2025, 42(3): 130–136. DOI： 10.12052/gdutxb.230202

Kuang Yongnian, Wang Feng, Ding Ke. Video Frame Anomaly Behavior Detection Method Based on Masked Convolution and External Attention[J]. 2025, 42(3): 130-136. DOI： 10.12052/gdutxb.230202.

摘要

针对视频异常行为检测难题，本文提出一种基于掩蔽卷积和外部注意力机制的卷积神经网络检测算法模型。一方面，通过掩蔽卷积限制有效卷积区域使神经网络高效学习，以此生成对正常行为特征有效的正态化建模。另一方面，通过结合轻量化的外部注意力机制，提高对感兴趣区域的建模质量。针对重构或预测架构的卷积神经网络，添加特征重构损失函数，提高视频异常行为检测的检测正确率。实验结果表明，在Avenue数据集、UCSD-Ped1数据集以及UCSD-Ped2数据集上，本文的检测方法分别提高了2.86%、2.54%以及0.56%的视频异常行为检测正确率。

Abstract

In order to solve the challenges of video anomaly behavior detection

a new detection algorithm model is proposed based on masked convolution and external attention mechanism convolutional neural network. On the one hand

by masked convolution

it can restrict the effective regions of convolution to make the neural network efficiently learn

so as to model the positive moderate that is effective in the feathers of normal behavior. On the other hand

by combining with lightweight external attention mechanisms

the modeling quality of interested regions is improved. Convolutional neural network in reconstruction or predictive architectures is added in feature reconstruction loss to improve the detection accuracy of video abnormal behavior detection. The experimental results show that the proposed method can effectively improve the detection performance of video anomaly behaviors by 2.86%

2.54% and 0.56% on the Avenue dataset

UCSD-Ped1 dataset and UCSD-Ped2 dataset

respectively.

关键词

Keywords

references

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