Moving Object Detection for Event-based Vision using k-means Clustering

TL;DR

This paper explores using k-means clustering to detect moving objects in event-based camera data, addressing challenges like noise sensitivity and low resolution inherent to such sensors.

Contribution

It introduces a novel application of k-means clustering specifically tailored for moving object detection in event-based vision data.

Findings

Effective detection of moving objects demonstrated

Improved robustness against noise in event data

Potential for real-time applications

Abstract

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.