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arXiv · 2109.01879

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

Anindya Mondal·Mayukhmali Das
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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.

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BibTeXRIS

Anindya Mondal, Mayukhmali Das. 2021-09-04. Moving Object Detection for Event-based Vision using k-means Clustering. https://doi.org/10.1109/upcon52273.2021.9667636

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