C3I-SynMicrosaccade:一个管道和数据集,用于使用神经形态事件摄像头流识别microsaccade
Waseem Shariff1, Timothy Hanley2, Maciej Stec1
1School of Electrical and Electronics Engineering, University of Galway, Ireland.
Data in brief
|February 9, 2026
概括
为了基于事件的建模,创建了一个新的微分眼 (小,快速的眼动) 合成数据集. 这种平衡的数据集有助于微小的识别和神经形态视觉研究.
科学领域:
- 计算机视觉 计算机视觉
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
背景情况:
- 微型鱼对视觉感知至关重要,并且很难建模.
- 现有的数据集可能缺乏基于事件的分析所需的特定特征.
研究的目的:
- 引入C3I-SynMicrosaccade数据集,用于基于事件的微分流模型和分类.
- 为训练和评估算法提供一个大规模,平衡的数据集.
主要方法:
- 使用Blender生成高分辨率RGB微秒序列,具有特定的轨迹和角度类.
- 使用v2e模拟器将RGB转换为基于事件的数据流,模仿神经形态传感器.
- 控制时间持续时间和事件计数用于类平衡和偏差消除.
主要成果:
- 创建了一个由175,000个事件序列组成的合成数据集,用于微时序.
- 数据集具有七个不同的角类 (0.5°到2.0°) 与受控的运动动态.
- 确保了阶级平衡,消除了对运动大小的偏见.
结论:
- C3I-SynMicrosaccade数据集提供了一个可控的,可重复的框架来研究定位眼动.
- 在精细运动动态下,能够对基于事件的算法进行可靠的评估.
- 促进了微小小的识别,神经形态视觉和合成到真实转移学习的研究.
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