Cross-Epoch Learning for Weakly Supervised Anomaly Detection in Surveillance Videos
Shenghao Yu, Chong Wang, Qiaomei Mao, Yuqi Li, Jiafei Wu
Abstract
Weakly Supervised Anomaly Detection (WSAD) in surveillance videos is a complex task since usually only video-level annotations are available. Previous work treated it as a regression problem by giving different scores on normal and anomaly events. However, the widely used mini-batch training strategy may suffer from the data imbalance between these two types of events, which limits the model's performance. In this work, a cross-epoch learning (XEL) strategy associated with a hard instance bank (HIB) is proposed to introduce additional information from previous training epochs. Two new losses are proposed for XEL to achieve a higher detection rate as well as a lower false alarm rate of anomaly events. Moreover, the proposed XEL can be directly integrated into any existing WSAD framework. Experimental results of three XEL embedded models have shown promising AUC improvement (3%~7%) on two public datasets, surpassing the state-of-the-art methods. Our code is available at: https://github.com/sdjsngs/XEL-WSAD.