Localizing Anomalies from Weakly-Labeled Videos

TL;DR

This paper introduces a weakly supervised method for localizing anomalous segments in videos, utilizing high-order context encoding to improve temporal localization accuracy, and demonstrates state-of-the-art results on benchmark datasets.

Contribution

The paper proposes a novel weakly supervised anomaly localization approach with high-order context encoding and a new traffic anomaly dataset, advancing the accuracy of temporal anomaly detection.

Findings

Achieves state-of-the-art performance on UCF-Crime and TAD datasets.

Effectively localizes anomalous segments using high-order context encoding.

Enhances anomaly detection with a new traffic anomaly dataset.

Abstract

Video anomaly detection under video-level labels is currently a challenging task. Previous works have made progresses on discriminating whether a video sequencecontains anomalies. However, most of them fail to accurately localize the anomalous events within videos in the temporal domain. In this paper, we propose a Weakly Supervised Anomaly Localization (WSAL) method focusing on temporally localizing anomalous segments within anomalous videos. Inspired by the appearance difference in anomalous videos, the evolution of adjacent temporal segments is evaluated for the localization of anomalous segments. To this end, a high-order context encoding model is proposed to not only extract semantic representations but also measure the dynamic variations so that the temporal context could be effectively utilized. In addition, in order to fully utilize the spatial context information, the immediate semantics are directly derived from the segment representations. The dynamic variations as well as the immediate semantics, are efficiently aggregated to obtain the final anomaly scores. An enhancement strategy is further proposed to deal with noise interference and the absence of localization guidance in anomaly detection. Moreover, to facilitate the diversity requirement for anomaly detection benchmarks, we also collect a new traffic anomaly (TAD) dataset which specifies in the traffic conditions, differing greatly from the current popular anomaly detection evaluation benchmarks.Extensive experiments are conducted to verify the effectiveness of different components, and our proposed method achieves new state-of-the-art performance on the UCF-Crime and TAD datasets.