Moving Object Detection for Event-based Vision using k-means Clustering
This addresses a difficult problem for event-based camera users, but it appears incremental as it adapts an existing method to a new domain.
The paper tackled moving object detection in event-based vision by applying k-means clustering to event-based data, which lacks texture and color features, but no specific results or numbers were reported.
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.