As a cutting-edge biosensor, the event camera holds significant potential in the field of computer vision, particularly regarding privacy preservation. However, compared to traditional cameras, event streams often contain noise and possess extremely sparse semantics, posing a formidable challenge for event-based person re-identification (event Re-ID). To address this, we introduce a novel event person re-identification network: the Spectrum-guided Feature Enhancement Network (SFE-Net). This network consists of two innovative components: the Multi-grain Spectrum Attention Mechanism (MSAM) and the Consecutive Patch Dropout Module (CPDM). MSAM employs a fourier spectrum transform strategy to filter event noise, while also utilizing an event-guided multi-granularity attention strategy to enhance and capture discriminative person semantics. CPDM employs a consecutive patch dropout strategy to generate multiple incomplete feature maps, encouraging the deep Re-ID model to equally perceive each effective region of the person's body and capture robust person descriptors. Extensive experiments on Event Re-ID datasets demonstrate that our SFE-Net achieves the best performance in this task.

翻译：作为一种前沿生物传感器，事件相机在计算机视觉领域，尤其是在隐私保护方面具有重要潜力。然而，与传统相机相比，事件流通常包含噪声且语义极其稀疏，这给基于事件的行人重识别（事件Re-ID）带来了严峻挑战。为解决这一问题，我们提出了一种新颖的事件行人重识别网络：光谱引导特征增强网络（SFE-Net）。该网络包含两个创新组件：多粒度光谱注意力机制（MSAM）和连续块丢弃模块（CPDM）。MSAM采用傅里叶光谱变换策略过滤事件噪声，同时利用事件引导的多粒度注意力策略增强并捕获具有判别力的行人语义。CPDM采用连续块丢弃策略生成多个不完整特征图，促使深度Re-ID模型均等感知行人身体的每个有效区域，并提取鲁棒的行人描述符。在事件Re-ID数据集上的大量实验表明，我们的SFE-Net在该任务中达到了最佳性能。