使用基于事件的聚类来估计速度和颜色
Xavier Lesage1,2, Rosalie Tran1, Stéphane Mancini1
1Univ. Grenoble Alpes, CNRS (National Centre for Scientific Research), Grenoble INP (Institute of Engineering), TIMA (Techniques of Informatics and Microelectronics for Integrated Systems Architecture), F-38000 Grenoble, France.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
基于事件的聚类使用事件传感器提取场景特征. 像对象速度和颜色这样的新属性改善了对象分类,提供了低功耗嵌入式解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 嵌入式系统 嵌入式系统
- 传感器技术 传感器技术
背景情况:
- 基于事件的传感器提供低功耗,高时间分辨率的数据捕获.
- 传统的集群算法可以适应基于事件的数据.
- 从事件数据中提取更丰富的属性对于高级应用程序至关重要.
研究的目的:
- 通过结合新的事件属性来增强基于事件的聚类.
- 从事件流中估计物体速度和颜色信息.
- 为了证明这些增强功能在对象分类中的实用性.
主要方法:
- 使用基于事件的图像传感器进行不均的采样和强度变化测量.
- 开发了用于聚类类似事件和估计运动和颜色等属性的算法.
- 采用定制的基于事件的模拟器生成户外场景视频进行测试.
主要成果:
- 速度估计取得了令人满意的结果,平衡精度和收速度.
- 染色度的估计是精确的,而发光度的估计提出了挑战.
- 增强的功能被证明足以准确地将对象分为预定义的类别.
结论:
- 基于事件的聚类与添加的属性提供了一个可行的低功耗解决方案,用于特征提取.
- 来自事件数据的对象速度和颜色估计显示出对现实世界的应用有希望.
- 进一步的研究可以改进亮度估计,并探索各种应用场景.
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