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

arXiv: https://arxiv.org/abs/2109.01879 · HTML: https://mlsanity.com/paper/2109.01879/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 4 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018)
- [Graph-Based Object Classification for Neuromorphic Vision Sensing](https://mlsanity.com/paper/1908.06648/) (2019)
- [Event-based Motion Segmentation with Spatio-Temporal Graph Cuts](https://mlsanity.com/paper/2012.08730/) (2020)
- [Distance Surface for Event-Based Optical Flow](https://mlsanity.com/paper/2003.12680/) (2020)
- [Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing](https://mlsanity.com/paper/1910.03579/) (2019)
- [GraphBGS: Background Subtraction via Recovery of Graph Signals](https://mlsanity.com/paper/2001.06404/) (2020)

## Built on by (most cited first, 2 of 2)

- [Recovery of Missing Sensor Data by Reconstructing Time-varying Graph Signals](https://mlsanity.com/paper/2203.00418/) (2022)
- [Real-time Unsupervised Object Discovery from Asynchronous Event Streams](https://mlsanity.com/paper/2608.26644/) (2026)

## Abstract (arXiv)

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.