构建多阈值事件相机数据集，提升行人车辆检测在动态环境中的适应性。
Event-Based Crossing Dataset (EBCD)
- 采集10组不同阈值下的事件数据，实现对稀疏性与噪声的精细控制。
- 在阈值4至75下测试主流模型，发现低阈值提升细节但引入噪声。
- 适合研究低延迟、高精度事件视觉系统及鲁棒目标检测的团队使用。
事件视觉通过捕捉异步强度变化而非静态帧，实现超快时间分辨率、稀疏数据编码和增强运动感知。然而，传统事件数据集采用固定阈值，难以适应真实环境波动：低阈值保留更多细节但噪声显著，高阈值抑制冗余激活却损失关键信息。为此，我们提出事件交叉数据集（EBCD），专用于动态户外环境中行人与车辆检测，采用多阈值框架优化事件表征。该数据集在10个阈值（4, 8, 12, 16, 20, 30, 40, 50, 60, 75）下采集事件图像，支持对不同稀疏性与噪声抑制条件下目标检测性能的全面评估。我们以YOLOv4、YOLOv7、EfficientDet-b0、MobileNet-v1和HOG等先进检测架构为基准，在阈值变化影响下进行实验分析。通过系统化阈值调节，我们推动事件视觉检测评估向更自适应方向发展，促进类脑视觉与真实场景动态的对齐。数据集已公开，网址：https://ieee-dataport.org/documents/event-based-crossing-dataset-ebcd。
原文摘要 · Abstract (English)
Event-based vision revolutionizes traditional image sensing by capturing asynchronous intensity variations rather than static frames, enabling ultrafast temporal resolution, sparse data encoding, and enhanced motion perception. While this paradigm offers significant advantages, conventional event-based datasets impose a fixed thresholding constraint to determine pixel activations, severely limiting adaptability to real-world environmental fluctuations. Lower thresholds retain finer details but introduce pervasive noise, whereas higher thresholds suppress extraneous activations at the expense of crucial object information. To mitigate these constraints, we introduce the Event-Based Crossing Dataset (EBCD), a comprehensive dataset tailored for pedestrian and vehicle detection in dynamic outdoor environments, incorporating a multi-thresholding framework to refine event representations. By capturing event-based images at ten distinct threshold levels (4, 8, 12, 16, 20, 30, 40, 50, 60, and 75), this dataset facilitates an extensive assessment of object detection performance under varying conditions of sparsity and noise suppression. We benchmark state-of-the-art detection architectures-including YOLOv4, YOLOv7, EfficientDet-b0, MobileNet-v1, and Histogram of Oriented Gradients (HOG)-to experiment upon the nuanced impact of threshold selection on detection performance. By offering a systematic approach to threshold variation, we foresee that EBCD fosters a more adaptive evaluation of event-based object detection, aligning diverse neuromorphic vision with real-world scene dynamics. We present the dataset as publicly available to propel further advancements in low-latency, high-fidelity neuromorphic imaging: https://ieee-dataport.org/documents/event-based-crossing-dataset-ebcd
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。