arXiv 2510.242312025-10-28cs.CV

基于事件相机的微眼跳识别基准测试：新型数据集与评估

Benchmarking Microsaccade Recognition with Event Cameras: A Novel Dataset and Evaluation

C3I Imaging Lab, School of Engineering, University of Galway, Ireland（C3I成像实验室，工程学院，Galway大学，爱尔兰）；School of Computer Science, University of Galway, Ireland（计算机科学学院，Galway大学，爱尔兰）；Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland（电子与电气工程系，Trinity College Dublin大学，爱尔兰）

Waseem Shariff, Timothy Hanley, Maciej Stec, Hossein Javidnia, Peter Corcoran

备注：: Accepted in British Machine Vision Conference (BMVC) 2025, Main Conference

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AI总结：

本研究针对传统微眼跳研究成本高、时间分辨率有限的问题，构建了首个基于事件的微眼跳数据集，提出光流增强的脉冲神经网络模型，实现约90%的分类准确率，为事件视觉研究建立基准。

摘要：

微眼跳是微小的非自主眼球运动，对视觉感知和神经处理至关重要。传统微眼跳研究通常使用眼动仪或基于帧的分析方法，这类方法虽然精度高，但成本高昂，且在可扩展性和时间分辨率上存在局限。基于事件的传感技术能高效捕获细粒度时空变化，提供了一种高速、低延迟的替代方案。\n本研究推出了首个基于事件的微眼跳数据集，以支持认知计算领域中小幅度眼球运动动力学的相关研究。我们使用Blender渲染高保真眼球运动场景，模拟角位移在0.5度到2.0度之间的微眼跳，将其划分为7个不同类别。这些场景通过v2e转换为事件流，保留了微眼跳的自然时间动力学特征，其持续时间在0.25毫秒到2.25毫秒之间。\n我们使用Spiking-VGG11、Spiking-VGG13和Spiking-VGG16对该数据集进行评估，并提出了Spiking-VGG16Flow——一种在SpikingJelly中实现的、经光流增强的变体模型。这些模型取得了约90%的平均准确率，能够成功根据角位移对微眼跳进行分类，且分类效果不受事件数量或持续时间的影响。这些结果证明了脉冲神经网络在精细运动识别方面的潜力，也为基于事件的视觉研究建立了基准。该数据集、代码和训练好的模型将在https://waseemshariff126.github.io/microsaccades/ 公开发布。

英文摘要：

Microsaccades are small, involuntary eye movements vital for visual perception and neural processing. Traditional microsaccade studies typically use eye trackers or frame-based analysis, which, while precise, are costly and limited in scalability and temporal resolution. Event-based sensing offers a high-speed, low-latency alternative by capturing fine-grained spatiotemporal changes efficiently. This work introduces a pioneering event-based microsaccade dataset to support research on small eye movement dynamics in cognitive computing. Using Blender, we render high-fidelity eye movement scenarios and simulate microsaccades with angular displacements from 0.5 to 2.0 degrees, divided into seven distinct classes. These are converted to event streams using v2e, preserving the natural temporal dynamics of microsaccades, with durations ranging from 0.25 ms to 2.25 ms. We evaluate the dataset using Spiking-VGG11, Spiking-VGG13, and Spiking-VGG16, and propose Spiking-VGG16Flow, an optical-flow-enhanced variant implemented in SpikingJelly. The models achieve around 90 percent average accuracy, successfully classifying microsaccades by angular displacement, independent of event count or duration. These results demonstrate the potential of spiking neural networks for fine motion recognition and establish a benchmark for event-based vision research. The dataset, code, and trained models will be publicly available at https://waseemshariff126.github.io/microsaccades/ .

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。