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.

本文提出了一种弱监督异常定位方法，利用高阶上下文编码模型提取语义表示，度量动态变化并有效利用时间上下文，通过聚合动态变量和立即语义来获取最终的异常分数，并通过增强策略处理噪声干扰和异常检测中的定位指导缺失，同时收集了一组新的交通异常数据集（TAD），实验表明该方法在常见的异常检测基准测试集中实现了最新的最佳性能。