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<p>SpikeCV is a new open-source computer vision platform designed for the spike camera, a neuromorphic visual sensor that has seen rapid development in recent years. Unlike traditional cameras, the spike camera operates without a global shutter; instead, each pixel accumulates light intensity and asynchronously fires spikes when a preset threshold is reached. These binary spikes can be generated at frequencies up to 40000 Hz, offering a new mode of visual representation with high spatiotemporal resolution, capturing continuous visual information from the environment. Taking advantage of the spike camera’s low latency and high dynamic range, many spike-based algorithms have made significant progress, such as high-quality imaging and ultra-high-speed target detection. To foster a collaborative ecosystem for spike vision, SpikeCV provides ultra-high-speed scene datasets, hardware interfaces, and a comprehensive module library. It emphasizes data encapsulation, standardization of dataset interfaces, modularity for vision tasks, and support for real-time applications in challenging scenarios. Unlike existing vision frameworks, SpikeCV is specifically optimized for spike data and real-time processing, addressing the unique requirements of spike-based systems. With the advent of the open-source Python ecosystem, modules of SpikeCV can be used as a Python library to fulfill most of the numerical analysis needs of researchers. We demonstrate the efficiency of the SpikeCV on offline inference and real-time applications. The project repository address and website are <a href="https://openi.pcl.ac.cn/Cordium/SpikeCV">https://openi.pcl.ac.cn/Cordium/SpikeCV</a> and <a href="https://spikecv.github.io">https://spikecv.github.io</a>.</p>

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SpikeCV: open a continuous computer vision era

  • 期刊论文

  • 发表日期：

    2026年1月14日

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  • Yajing Zheng,
  • Jiyuan Zhang,
  • Rui Zhao,
  • Jianhao Ding,
  • Shiyan Chen,
  • Weijian Wu,
  • Ruiqin Xiong,
  • Zhaofei Yu,
  • Tiejun Huang

摘要

SpikeCV is a new open-source computer vision platform designed for the spike camera, a neuromorphic visual sensor that has seen rapid development in recent years. Unlike traditional cameras, the spike camera operates without a global shutter; instead, each pixel accumulates light intensity and asynchronously fires spikes when a preset threshold is reached. These binary spikes can be generated at frequencies up to 40000 Hz, offering a new mode of visual representation with high spatiotemporal resolution, capturing continuous visual information from the environment. Taking advantage of the spike camera’s low latency and high dynamic range, many spike-based algorithms have made significant progress, such as high-quality imaging and ultra-high-speed target detection. To foster a collaborative ecosystem for spike vision, SpikeCV provides ultra-high-speed scene datasets, hardware interfaces, and a comprehensive module library. It emphasizes data encapsulation, standardization of dataset interfaces, modularity for vision tasks, and support for real-time applications in challenging scenarios. Unlike existing vision frameworks, SpikeCV is specifically optimized for spike data and real-time processing, addressing the unique requirements of spike-based systems. With the advent of the open-source Python ecosystem, modules of SpikeCV can be used as a Python library to fulfill most of the numerical analysis needs of researchers. We demonstrate the efficiency of the SpikeCV on offline inference and real-time applications. The project repository address and website are https://openi.pcl.ac.cn/Cordium/SpikeCV and https://spikecv.github.io.