  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.
