# Real-time clustering and multi-target tracking using event-based sensors

arXiv: https://arxiv.org/abs/1807.02851 · HTML: https://mlsanity.com/paper/1807.02851/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2018.

## Built on by (most cited first, 23 of 23)

- [Object Tracking by Jointly Exploiting Frame and Event Domain](https://mlsanity.com/paper/2109.09052/) (2021)
- [SpikeMS: Deep Spiking Neural Network for Motion Segmentation](https://mlsanity.com/paper/2105.06562/) (2021)
- [Repurposing Pre-trained Video Diffusion Models for Event-based Video Interpolation](https://mlsanity.com/paper/2412.07761/) (2024)
- [eCDT: Event Clustering for Simultaneous Feature Detection and Tracking-](https://mlsanity.com/paper/2207.09108/) (2022)
- [Asynchronous Blob Tracker for Event Cameras](https://mlsanity.com/paper/2307.10593/) (2023)
- [EventHDR: from Event to High-Speed HDR Videos and Beyond](https://mlsanity.com/paper/2409.17029/) (2024)
- [Distractor-aware Event-based Tracking](https://mlsanity.com/paper/2310.14194/) (2023)
- [A Neuromorphic Dataset for Object Segmentation in Indoor Cluttered Environment](https://mlsanity.com/paper/2302.06301/) (2023)
- [Multi-domain Collaborative Feature Representation for Robust Visual Object Tracking](https://mlsanity.com/paper/2108.04521/) (2021)
- [SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features](https://mlsanity.com/paper/2309.16987/) (2023)
- [PUCK: Parallel Surface and Convolution-kernel Tracking for Event-Based Cameras](https://mlsanity.com/paper/2205.07657/) (2022)
- [WorldGen: A Large Scale Generative Simulator](https://mlsanity.com/paper/2210.00715/) (2022)
- [Neuromorphic Vision-based Motion Segmentation with Graph Transformer Neural Network](https://mlsanity.com/paper/2404.10940/) (2024)
- [High-temporal-resolution event-based vehicle detection and tracking](https://mlsanity.com/paper/2212.14289/) (2022)
- [TimeRewind: Rewinding Time with Image-and-Events Video Diffusion](https://mlsanity.com/paper/2403.13800/) (2024)
- [Dynamic Event-based Optical Identification and Communication](https://mlsanity.com/paper/2303.07169/) (2023)
- [Asynchronous Multi-Object Tracking with an Event Camera](https://mlsanity.com/paper/2505.08126/) (2025)
- [Event-based Vision for Early Prediction of Manipulation Actions](https://mlsanity.com/paper/2307.14332/) (2023)
- [Event Data Association via Robust Model Fitting for Event-based Object Tracking](https://mlsanity.com/paper/2110.12962/) (2021)
- [Event-based tracking of human hands](https://mlsanity.com/paper/2304.06534/) (2023)
- [Event Camera as Region Proposal Network](https://mlsanity.com/paper/2305.00718/) (2023)
- [Event Driven Clustering Algorithm](https://mlsanity.com/paper/2602.00115/) (2026)
- [Real-time Unsupervised Object Discovery from Asynchronous Event Streams](https://mlsanity.com/paper/2608.26644/) (2026)

## Abstract (arXiv)

Clustering is crucial for many computer vision applications such as robust tracking, object detection and segmentation. This work presents a real-time clustering technique that takes advantage of the unique properties of event-based vision sensors. Since event-based sensors trigger events only when the intensity changes, the data is sparse, with low redundancy. Thus, our approach redefines the well-known mean-shift clustering method using asynchronous events instead of conventional frames. The potential of our approach is demonstrated in a multi-target tracking application using Kalman filters to smooth the trajectories. We evaluated our method on an existing dataset with patterns of different shapes and speeds, and a new dataset that we collected. The sensor was attached to the Baxter robot in an eye-in-hand setup monitoring real-world objects in an action manipulation task. Clustering accuracy achieved an F-measure of 0.95, reducing the computational cost by 88% compared to the frame-based method. The average error for tracking was 2.5 pixels and the clustering achieved a consistent number of clusters along time.