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Localizing Anomalies from Weakly-Labeled Videos

About

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.

Hui Lv, Chuanwei Zhou, Chunyan Xu, Zhen Cui, Jian Yang• 2020

Related benchmarks

TaskDatasetResultRank
Video Anomaly DetectionUCF-Crime
AUC85.38
129
Anomaly DetectionUCF-Crime (test)
AUC0.8538
99
Video Anomaly DetectionShanghaiTech--
51
Weakly Supervised Video Anomaly DetectionUCF-Crime
AUC85.38
18
Anomaly DetectionTAD (test)
Overall AUC89.64
14
Temporal Anomaly DetectionTAD
AUC (%)89.64
10
Anomaly DetectionUCF-Crime anomaly
Anomaly Subset AUC67.38
5
Road Accident DetectionSurveillance-based accident dataset (test)
Accuracy64.7
5
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