计算机科学
异常检测
水准点(测量)
帧(网络)
入侵检测系统
人工智能
纵火
异常(物理)
机器学习
数据挖掘
物理
电信
社会学
犯罪学
凝聚态物理
地理
大地测量学
作者
Qun Li,Rui Yang,Fu Xiao,Bir Bhanu,Feng Zhang
标识
DOI:10.1016/j.knosys.2022.109348
摘要
Anomaly detection is one of the most challenging tasks in visual understanding because anomalous events are diverse and complicated. In this paper, we propose a future frame prediction framework and a Multiple Instance Learning (MIL) framework by leveraging attention schemes to learn anomalies. In both frameworks, we utilize the attention-based module to better localize anomalies. Further, we introduce a memory addressing module for the future frame prediction framework, and a novel loss function for the MIL framework, respectively. We also introduce a new multi-view dataset of 170 videos with 10 realistic anomalies that pose a serious threat to security such as intrusion, crowd, accident, weapon, arson, etc., as well as normal activities. The experimental results demonstrate the effectiveness of our methods on the proposed dataset and multiple benchmark datasets. The proposed dataset brings more opportunities and challenges to future work on anomaly detection.
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