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

Source: [Moving Object Detection for Event-based Vision using k-means Clustering](<https://www.sota2.com/research/paper/68fa6b9c1e209131cea2f4ef>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:12:33.795Z

Authors: Anindya Mondal, Mayukhmali Das

Published: 2021-09-04T14:43:14.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2109.01879>)

[PDF](<https://arxiv.org/pdf/2109.01879.pdf>)