Abstract
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.
Abstract (translated)
视频异常检测在智能监控系统中扮演着重要角色。为了提升模型的异常识别能力，以往的研究通常会利用RGB图像、光流和文本特征。最近，动态视觉传感器（DVS）作为一种有前景的技术崭露头角，它能够以离散事件的形式捕捉具有极高动态范围和时间分辨率的视觉信息。这相较于传统摄像头而言，可以减少数据冗余并增强对移动物体的捕获能力。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——UCF-Crime-DVS。 为了充分利用这一新的数据模式，基于脉冲神经网络（SNNs）设计了一种多尺度脉冲融合网络（MSF）。这项工作探索了事件数据中的动态信息在视频异常检测中的潜在应用。我们的实验展示了该框架在UCF-Crime-DVS上的有效性，并且与其它模型相比表现出更优异的性能，为基于SNN的弱监督视频异常检测建立了新的基准。
URL
https://arxiv.org/abs/2503.12905
