# Moving Object Detection for Event-based vision using Graph Spectral Clustering

arXiv: https://arxiv.org/abs/2109.14979 · HTML: https://mlsanity.com/paper/2109.14979/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 30 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [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)
- [Graph Learning: A Survey](https://mlsanity.com/paper/2105.00696/) (2021)
- [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018)
- [Event-Based Motion Segmentation by Motion Compensation](https://mlsanity.com/paper/1904.01293/) (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)
- [FAST-Dynamic-Vision: Detection and Tracking Dynamic Objects with Event and Depth Sensing](https://mlsanity.com/paper/2103.05903/) (2021)
- [GraphBGS: Background Subtraction via Recovery of Graph Signals](https://mlsanity.com/paper/2001.06404/) (2020)

## Built on by (most cited first, 28 of 28)

- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection](https://mlsanity.com/paper/2304.13455/) (2023)
- [Event Camera Data Pre-training](https://mlsanity.com/paper/2301.01928/) (2023)
- [Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackers](https://mlsanity.com/paper/2307.04129/) (2023)
- [RGB-Event Fusion for Moving Object Detection in Autonomous Driving](https://mlsanity.com/paper/2209.08323/) (2022)
- [Emergent Visual Sensors for Autonomous Vehicles](https://mlsanity.com/paper/2205.09383/) (2022)
- [Neuromorphic Event-based Facial Expression Recognition](https://mlsanity.com/paper/2304.06351/) (2023)
- [Video Anomaly Detection in 10 Years: A Survey and Outlook](https://mlsanity.com/paper/2405.19387/) (2024)
- [DOTIE - Detecting Objects through Temporal Isolation of Events using a Spiking Architecture](https://mlsanity.com/paper/2210.00975/) (2022)
- [Distractor-aware Event-based Tracking](https://mlsanity.com/paper/2310.14194/) (2023)
- [An Asynchronous Linear Filter Architecture for Hybrid Event-Frame Cameras](https://mlsanity.com/paper/2309.01159/) (2023)
- [Riemannian Optimization on Relaxed Indicator Matrix Manifold](https://mlsanity.com/paper/2503.20505/) (2025)
- [Time-varying Signals Recovery via Graph Neural Networks](https://mlsanity.com/paper/2302.11313/) (2023)
- [High-temporal-resolution event-based vehicle detection and tracking](https://mlsanity.com/paper/2212.14289/) (2022)
- [evTransFER: A Transfer Learning Framework for Event-based Facial Expression Recognition](https://mlsanity.com/paper/2508.03609/) (2025)
- [Recovery of Missing Sensor Data by Reconstructing Time-varying Graph Signals](https://mlsanity.com/paper/2203.00418/) (2022)
- [On the Theory of Bias Tuning in Event Cameras](https://mlsanity.com/paper/2501.18788/) (2025)
- [TUMTraf EMOT: Event-Based Multi-Object Tracking Dataset and Baseline for Traffic Scenarios](https://mlsanity.com/paper/2512.14595/) (2025)
- [Real-Time Drone Detection in Event Cameras via Per-Pixel Frequency Analysis](https://mlsanity.com/paper/2603.08386/) (2026)
- [Estimation of a Causal Directed Acyclic Graph Process using Non-Gaussianity](https://mlsanity.com/paper/2211.13800/) (2022)
- [Event Camera as Region Proposal Network](https://mlsanity.com/paper/2305.00718/) (2023)
- [Modularity aided consistent attributed graph clustering via coarsening](https://mlsanity.com/paper/2407.07128/) (2024)
- [Event Driven Clustering Algorithm](https://mlsanity.com/paper/2602.00115/) (2026)
- [Following the Flow: Advection-Consistent Modeling for Event-based Small Object Detection](https://mlsanity.com/paper/2606.22378/) (2026)
- [E-TraMamba: A New Paradigm for Efficient Long-Term 3D Feature Tracking with Event Cameras](https://mlsanity.com/paper/2607.02866/) (2026)
- [Real-time Unsupervised Object Discovery from Asynchronous Event Streams](https://mlsanity.com/paper/2608.26644/) (2026)
- [PointEvent: Rethinking Event-based Tiny Object Detection via Serialized Motion Evidence Accumulation](https://mlsanity.com/paper/2609.20066/) (2026)
- [The Shape of Events: Edge-Based Inductive Biases via Cross-Domain Distillation](https://mlsanity.com/paper/2609.30478/) (2026)

## Abstract (arXiv)

Moving object detection has been a central topic of discussion in computer vision for its wide range of applications like in self-driving cars, video surveillance, security, and enforcement. Neuromorphic Vision Sensors (NVS) are bio-inspired sensors that mimic the working of the human eye. Unlike conventional frame-based cameras, these sensors capture a stream of asynchronous 'events' that pose multiple advantages over the former, like high dynamic range, low latency, low power consumption, and reduced motion blur. However, these advantages come at a high cost, as the event camera data typically contains more noise and has low resolution. Moreover, as event-based cameras can only capture the relative changes in brightness of a scene, event data do not contain usual visual information (like texture and color) as available in video data from normal cameras. So, moving object detection in event-based cameras becomes an extremely challenging task. In this paper, we present an unsupervised Graph Spectral Clustering technique for Moving Object Detection in Event-based data (GSCEventMOD). We additionally show how the optimum number of moving objects can be automatically determined. Experimental comparisons on publicly available datasets show that the proposed GSCEventMOD algorithm outperforms a number of state-of-the-art techniques by a maximum margin of 30%.