论文标题
从弱标记的视频中定位异常
Localizing Anomalies from Weakly-Labeled Videos
论文作者
论文摘要
视频级标签下的视频异常检测目前是一项具有挑战性的任务。先前的工作已经取得了进步，以区分视频序列是否会引起异常。但是，他们中的大多数无法准确地将异常事件定位在时间域中的视频中。在本文中，我们提出了一个弱监督的异常定位（WSAL）方法，该方法着重于时间在异常视频中定位异常段。受异常视频的外观差异的启发，对相邻时间段的演变进行了评估，以定位异常的片段。为此，提出了一个高阶上下文编码模型，不仅提取语义表示，而且还测量动态变化，以便可以有效地利用时间上下文。此外，为了充分利用空间上下文信息，直接的语义直接源自段表示。动态变化以及直接的语义是有效地汇总的，以获得最终的异常得分。进一步提出了一种增强策略，以应对噪声干扰以及在异常检测中缺乏定位指导。 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.
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.
