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    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:

    • 事件摄像机提供高时间分辨率和动态范围,为计算机视觉任务提供新的可能性.
    • 与传统的视频数据相比,从稀疏的异步事件流中对物体进行细分具有独特的挑战.

    研究的目的:

    • 开发从事件流中连续时间对象细分的第一个框架.
    • 为了在具有稀疏事件数据的具有挑战性的条件下实现精确的对象细分.

    主要方法:

    • 一个新的框架,包含一个循环时间嵌入提取 (RTEE) 模块 (ResLSTM) 和一个跨时空特征建模 (CSFM) 模块 (变压器架构).
    • 经常处理历史事件和面具与当前事件,以不断更新时间嵌入.
    • 构建基于真实世界和模拟事件的对象细分数据集.

    主要成果:

    • 对于连续时间对象细分的反复架构的证明有效性.
    • 从稀疏的事件流中成功分割完整的对象.

    结论:

    • 拟议的框架有效地解决了在事件流中对象细分的挑战.
    • 这项工作推进了事件摄像机在需要动态对象跟踪和细分的现实应用中的功能.