计算机与数字工程2024，Vol.52Issue(4)：989-994,6.DOI:10.3969/j.issn.1672-9722.2024.04.006
基于深度学习和高斯混合模型的异常识别算法
Anomaly Detection Algorithm Based on Deep Learning and Gaussian Mixture Model
范冰 1李鹏 1金舒 1王志心 1王媛媛1
作者信息
- 1. 国电南京自动化股份有限公司研究院系统自动化技术与应用研究所 南京 210000
- 折叠
摘要
Abstract
Current approaches for anomaly detection cannot tackle both human-related abnormal behavior recognition and non-human anomaly recognition.Moreover,the detection accuracy is low especially in the case of occlusion.To solve this problem,the network based on GRU is proposed to train the structural feature segments of the human body posture in the normal frame.In do-ing so,this approach is able to anti-occlusion.This paper further introduces an improved Gaussian mixture model with adaptive K value to extract abnormal situations(such as cars,bicycles,remnants,etc.)that are not related to human.Experiments show that the anomaly detection AUC of proposed method increased by 0.025 and 0.034 on Avenue dataset and ShanghaiTech dataset respec-tively.关键词
高斯混合模型/异常行为识别/片段化训练/抗遮挡Key words
Gaussian mixture model/abnormal behavior recognition/segment training/anti-occlusion分类
信息技术与安全科学引用本文复制引用
范冰,李鹏,金舒,王志心,王媛媛..基于深度学习和高斯混合模型的异常识别算法[J].计算机与数字工程,2024,52(4):989-994,6.
