首个基于事件相机的异常检测数据集，推动神经网络在监控中的应用
UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks
- 用事件相机采集动态视觉数据，构建新基准数据集
- 提出多尺度脉冲融合网络，在新数据集上表现更优
- 适合做神经形态计算与智能监控研究的学者参考
视频异常检测在智能监控系统中至关重要。以往方法多依赖RGB图像、光流和文本特征。近年来，动态视觉传感器（DVS）因其高动态范围和时间分辨率，以离散事件形式捕捉视觉信息，减少数据冗余并增强运动目标捕获能力。为将此类动态信息引入监控领域，本文首次构建了基于事件相机的视频异常检测基准——UCF-Crime-DVS。为充分挖掘该模态潜力，设计了基于脉冲神经网络（SNN）的多尺度脉冲融合网络（MSF）。实验表明，该框架在UCF-Crime-DVS上有效，性能优于其他模型，建立了基于SNN的弱监督视频异常检测新基准。
原文摘要 · Abstract (English)
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.
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