Anomaly event detection has been extensively researched in computer vision in recent years. Most conventional anomaly event detection methods can only leverage the single-modal cues and not deal with the complementary information underlying other modalities in videos. To address this issue, in this work, we propose a novel two-stream convolutional networks model for anomaly detection in surveillance videos. Specifically, the proposed model consists of RGB and Flow two-stream networks, in which the final anomaly event detection score is the fusion of those of two networks. Furthermore, we consider two fusion situations, including the fusion of two streams with the same or different number of layers respectively. The design insight is to leverage the information underlying each stream and the complementary cues of RGB and Flow two-stream sufficiently. Two datasets (UCF-Crime and ShanghaiTech) are used to validate the effectiveness of proposed solution.

中文翻译：

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基于两流模型的安全监控中的异常事件检测

近年来，异常事件检测已在计算机视觉中得到了广泛的研究。大多数常规的异常事件检测方法只能利用单模式提示，而不能处理视频中其他模式下的补充信息。为了解决这个问题，在这项工作中，我们提出了一种新颖的两流卷积网络模型，用于监视视频中的异常检测。具体来说，所提出的模型由RGB和Flow两流网络组成，其中最终的异常事件检测分数是两个网络的融合。此外，我们考虑了两种融合情况，包括分别具有相同或不同层数的两个流的融合。设计的见解是充分利用每个流的基础信息以及RGB和Flow两流的互补线索。使用两个数据集（UCF-Crime和ShanghaiTech）来验证所提出解决方案的有效性。