重庆理工大学学报2024，Vol.38Issue(7)：243-249,7.DOI:10.3969/j.issn.1674-8425(z).2024.04.031
采用元学习的弱监督视频异常检测方法
Weakly supervised video anomaly detection method based on meta-learning
摘要
Abstract
Video anomaly detection usually involves many unknown scenarios, and current weak supervision methods only consider the accuracy of anomaly detection and ignore the generalization ability of unknown scenarios, resulting in poor performance when the model is transferred to a new scenario.To address the generalization problem of the model, this paper proposes a meta-learning based method.The core idea of the method is to learn an adaptive model through meta-learning and make the new model adapt to a new scenario quickly by designing multiple tasks.This method builds a two-stage video anomaly detection framework.In the inner phase, the detection accuracy of the basic detector is improved by reducing the internal loss function of the task.In the outer loop phase, the model is adapted to different tasks and the internal representation of the model is improved, so that it is easy to fine-tune quickly in new scenarios.The new method improves the generalization ability of the model to unseen scenarios without reducing the accuracy of the existing method, and greatly reduces the number of iterations and training time when the model transfers to the new scenarios.The number of training iterations on UCF-Crime dataset, XD-Violence dataset and UCSD Ped2 dataset is reduced to 105, 125 and 135 rounds respectively.关键词
视频异常检测/元学习/弱监督学习Key words
video anomaly detection/meta-learning/weakly supervised learning分类
信息技术与安全科学引用本文复制引用
张红民,栾小虎,粟建顺,颜鼎鼎..采用元学习的弱监督视频异常检测方法[J].重庆理工大学学报,2024,38(7):243-249,7.基金项目
重庆市自然科学基金面上项目(cstc2021 jcyj-msxmX0525,CSTB2022NSCQ-MSX0786,CSTB2023NSCQ-MSX0911) （cstc2021 jcyj-msxmX0525,CSTB2022NSCQ-MSX0786,CSTB2023NSCQ-MSX0911）
重庆市教委科学技术研究项目(KJQN202201109) （KJQN202201109）
