Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection

TL;DR

This paper introduces a self-trained deep ordinal regression method for end-to-end video anomaly detection that reduces dependence on labeled data and improves feature learning, achieving superior results on real-world videos.

Contribution

It proposes a novel end-to-end trainable approach using surrogate ordinal regression, eliminating the need for manually labeled normal/abnormal data in video anomaly detection.

Findings

Outperforms state-of-the-art methods requiring no labeled data

Enables accurate localization of anomalies

Supports effective human-in-the-loop detection

Abstract

Video anomaly detection is of critical practical importance to a variety of real applications because it allows human attention to be focused on events that are likely to be of interest, in spite of an otherwise overwhelming volume of video. We show that applying self-trained deep ordinal regression to video anomaly detection overcomes two key limitations of existing methods, namely, 1) being highly dependent on manually labeled normal training data; and 2) sub-optimal feature learning. By formulating a surrogate two-class ordinal regression task we devise an end-to-end trainable video anomaly detection approach that enables joint representation learning and anomaly scoring without manually labeled normal/abnormal data. Experiments on eight real-world video scenes show that our proposed method outperforms state-of-the-art methods that require no labeled training data by a substantial margin, and enables easy and accurate localization of the identified anomalies. Furthermore, we demonstrate that our method offers effective human-in-the-loop anomaly detection which can be critical in applications where anomalies are rare and the false-negative cost is high.