Weakly-Supervised Spatiotemporal Anomaly Detection

2026-05-13

AI Summary

This work addresses the challenge of high annotation costs and the availability of only video-level weak labels in video anomaly detection by proposing a novel weakly supervised approach that, for the first time, jointly models spatiotemporal local anomalies under such constraints. The method treats normal and anomalous videos as negative and positive bags, respectively, within a multiple instance learning (MIL) framework. It integrates spatiotemporal feature extraction with a classifier-driven anomaly scoring mechanism and introduces a multiple instance ranking loss to effectively leverage video-level labels for pixel-level anomaly localization. Experimental results on the UCF Crime2Local dataset demonstrate that the proposed method accurately detects localized spatiotemporal anomalies using only video-level supervision.

Abstract

In this paper, we explore a weakly supervised method for anomaly detection. Since annotating videos is time-consuming, we only look at weak video-level labels during training. This means that given a video, we know that it is either normal or contains an anomaly, but no further annotations are used to train the network. Features are extracted from video clips that are either normal or anomalous. These features are used to determine anomaly scores for spatiotemporal regions of the clips based on a classifier and the implementation of a multiple instance ranking loss (MIL). We represent both anomalous and normal video clips as positive and negative bags, respectively, to apply MIL. Furthermore, since anomalies are usually localized to a part of a frame rather than the whole frame, we chose to explore temporal as well as spatial anomaly detection. We show our results on the UCF Crime2Local Dataset, which contains spatiotemporal annotations for a portion of the UCF Crime Dataset.

Problem

• Weakly-Supervised Learning
• Spatiotemporal Anomaly Detection
• Video Anomaly Detection
• Multiple Instance Learning
• Anomaly Localization

Innovation

• weakly-supervised learning
• spatiotemporal anomaly detection
• multiple instance learning
• anomaly localization
• video-level labels