Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning

TL;DR

This paper introduces AR-Net, a weakly supervised video anomaly detection framework that uses regression with novel loss functions to improve discriminative feature learning, achieving state-of-the-art results on ShanghaiTech.

Contribution

The paper proposes a new weakly supervised anomaly detection method with a center-guided discriminative learning approach, including a dynamic multiple-instance loss and a center loss.

Findings

Achieves state-of-the-art performance on ShanghaiTech dataset.

Effectively distinguishes normal and anomalous video clips.

Demonstrates the effectiveness of discriminative feature learning in weak supervision.

Abstract

Anomaly detection in surveillance videos is a challenging task due to the diversity of anomalous video content and duration. In this paper, we consider video anomaly detection as a regression problem with respect to anomaly scores of video clips under weak supervision. Hence, we propose an anomaly detection framework, called Anomaly Regression Net (AR-Net), which only requires video-level labels in training stage. Further, to learn discriminative features for anomaly detection, we design a dynamic multiple-instance learning loss and a center loss for the proposed AR-Net. The former is used to enlarge the inter-class distance between anomalous and normal instances, while the latter is proposed to reduce the intra-class distance of normal instances. Comprehensive experiments are performed on a challenging benchmark: ShanghaiTech. Our method yields a new state-of-the-art result for video anomaly detection on ShanghaiTech dataset