Spatio-temporal predictive tasks for abnormal event detection in videos

TL;DR

This paper introduces new constrained pretext tasks for video anomaly detection that improve the learning of normality patterns by jointly predicting spatial and temporal features, outperforming existing methods.

Contribution

It proposes a novel approach using constrained pretext tasks to better learn object-level normality in videos for anomaly detection.

Findings

Outperforms current state-of-the-art on benchmark datasets

Effectively localizes and tracks anomalies in videos

Learns more robust normality patterns through joint spatial-temporal prediction

Abstract

Abnormal event detection in videos is a challenging problem, partly due to the multiplicity of abnormal patterns and the lack of their corresponding annotations. In this paper, we propose new constrained pretext tasks to learn object level normality patterns. Our approach consists in learning a mapping between down-scaled visual queries and their corresponding normal appearance and motion characteristics at the original resolution. The proposed tasks are more challenging than reconstruction and future frame prediction tasks which are widely used in the literature, since our model learns to jointly predict spatial and temporal features rather than reconstructing them. We believe that more constrained pretext tasks induce a better learning of normality patterns. Experiments on several benchmark datasets demonstrate the effectiveness of our approach to localize and track anomalies as it outperforms or reaches the current state-of-the-art on spatio-temporal evaluation metrics.