Moving object detection is important in computer vision. Event-based cameras are bio-inspired cameras that work by mimicking the working of the human eye. These cameras have multiple advantages over conventional frame-based cameras, like reduced latency, HDR, reduced motion blur during high motion, low power consumption, etc. In spite of these advantages, event-based cameras are noise-sensitive and have low resolution. Moreover, the task of moving object detection in these cameras is difficult, as event-based sensors lack useful visual features like texture and color. In this paper, we investigate the application of the k-means clustering technique in detecting moving objects in event-based data.
翻译:移动物体的探测在计算机视觉中很重要。 事件相机是仿照人类眼睛工作的生动摄像头。 这些相机比传统的框架相机具有多种优势,如低潜伏、人类发展报告、高运动期间的减动模糊、低电耗等。 尽管有这些优势,事件相机对噪音敏感,分辨率低。 此外,在这些摄像机中移动物体的探测任务很困难,因为事件感应器缺乏像纹理和颜色等有用的视觉特征。 在本文中,我们调查K手段集技术在根据事件数据探测移动物体时的应用。