  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.
