神经形态视觉传感器的硬件,算法和应用:一篇评论
Claudio Cimarelli1, Jose Andres Millan-Romera1, Holger Voos1,2
1Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
基于事件的摄像头提供稀疏,低延迟的数据流,以获得高时间分辨率. 这项调查综合了神经形态视觉的硬件,算法和应用,解决了挑战和未来的方向.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 传感器技术 传感器技术
背景情况:
- 传统的基于的相机以固定率捕获数据.
- 基于事件的 (神经形态) 摄像头报告了异步的像素亮度变化.
- 神经形相机提供稀疏,低延迟的数据,具有高时间分辨率.
研究的目的:
- 系统地审查神经形态视觉技术和应用.
- 为整合硬件,算法和现实世界的应用提供一个连贯的框架.
- 引导进入该领域的研究人员,并综合最近的进展.
主要方法:
- 检查了神经形相机的技术进化和硬件特性.
- 审查了针对基于事件的数据量身定制的图像处理算法.
- 介绍了事件摄像机使用的实际应用案例研究.
主要成果:
- 详细的硬件进步从开始到当前的模型.
- 涵盖了功能检测,跟踪,光流,深度估计和对象识别的算法.
- 突出了各种场景中的成功应用程序.
结论:
- 事件摄像头能够实现新的处理范式和应用程序.
- 确定了挑战,研究缺口和未来发展方向.
- 该调查为神经形态视觉研究人员提供了一个平衡的综合.
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