Video anomaly detection plays a significant role in intelligent surveillance systems. To enhance model's anomaly recognition ability, previous works have typically involved RGB, optical flow, and text features. Recently, dynamic vision sensors (DVS) have emerged as a promising technology, which capture visual information as discrete events with a very high dynamic range and temporal resolution. It reduces data redundancy and enhances the capture capacity of moving objects compared to conventional camera. To introduce this rich dynamic information into the surveillance field, we created the first DVS video anomaly detection benchmark, namely UCF-Crime-DVS. To fully utilize this new data modality, a multi-scale spiking fusion network (MSF) is designed based on spiking neural networks (SNNs). This work explores the potential application of dynamic information from event data in video anomaly detection. Our experiments demonstrate the effectiveness of our framework on UCF-Crime-DVS and its superior performance compared to other models, establishing a new baseline for SNN-based weakly supervised video anomaly detection.

翻译：视频异常检测在智能监控系统中扮演着重要角色。为提升模型的异常识别能力，先前的研究通常涉及RGB、光流及文本特征。近年来，动态视觉传感器（DVS）作为一种前景广阔的技术而兴起，它以离散事件的形式捕获视觉信息，具有极高的动态范围和时间分辨率。与传统相机相比，它减少了数据冗余并增强了对运动物体的捕捉能力。为了将这种丰富的动态信息引入监控领域，我们创建了首个DVS视频异常检测基准数据集，即UCF-Crime-DVS。为充分利用这一新型数据模态，我们基于脉冲神经网络（SNNs）设计了一种多尺度脉冲融合网络（MSF）。本研究探索了事件数据中动态信息在视频异常检测中的潜在应用。我们的实验证明了所提框架在UCF-Crime-DVS上的有效性及其相较于其他模型的优越性能，为基于SNN的弱监督视频异常检测建立了新的基准。