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.
翻译:视频标签下的视频异常现象探测目前是一项艰巨的任务。 先前的工作在区分视频序列是否含有异常现象方面取得了进展。 但是, 多数工作在区分视频序列是否含有异常现象方面取得了进展 。 但是, 大部分工作没有在时间域的视频中准确定位异常事件。 在本文中, 我们提议了一种薄弱的监视异常异常本地化(WSAL)方法, 重点是异常视频中暂时本地化异常现象部分。 受到异常视频外观差异的启发, 近邻时间段的演变被评估用于异常部分的本地化。 为此, 提议了一个高端背景编码模式, 不仅可以提取语义表达, 而且还可以测量动态变化, 以便有效利用时间范围环境。 此外, 为了充分利用空间背景信息, 直接的语义化方法直接源于非常规视频视频。 动态变异异以及直接语系变化, 为了获得最终异常分数,我们进一步提出了一个强化战略, 以应对噪音干扰和异常现象检测中缺乏本地化指导。 此外, 用于为当前异常现象检测而进行不同程度的地形测试, 也规定了对当前地形数据进行不同性分析的标准。