# Asynchronous Blob Tracker for Event Cameras

arXiv: https://arxiv.org/abs/2307.10593 · HTML: https://mlsanity.com/paper/2307.10593/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 59 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 23 of 23)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018)
- [Asynchronous, Photometric Feature Tracking using Events and Frames](https://mlsanity.com/paper/1807.09713/) (2018)
- [Object Tracking by Jointly Exploiting Frame and Event Domain](https://mlsanity.com/paper/2109.09052/) (2021)
- [Continuous-time Intensity Estimation Using Event Cameras](https://mlsanity.com/paper/1811.00386/) (2018)
- [Event Based, Near Eye Gaze Tracking Beyond 10,000Hz](https://mlsanity.com/paper/2004.03577/) (2020)
- [Speed Invariant Time Surface for Learning to Detect Corner Points with Event-Based Cameras](https://mlsanity.com/paper/1903.11332/) (2019)
- [EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Cameras](https://mlsanity.com/paper/1906.02919/) (2019)
- [Star Tracking using an Event Camera](https://mlsanity.com/paper/1812.02895/) (2018)
- [Real-Time Face & Eye Tracking and Blink Detection using Event Cameras](https://mlsanity.com/paper/2010.08278/) (2020)
- [FA-Harris: A Fast and Asynchronous Corner Detector for Event Cameras](https://mlsanity.com/paper/1906.10925/) (2019)
- [An Asynchronous Kalman Filter for Hybrid Event Cameras](https://mlsanity.com/paper/2012.05590/) (2020)
- [Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking](https://mlsanity.com/paper/2002.05583/) (2020)
- [Real-time clustering and multi-target tracking using event-based sensors](https://mlsanity.com/paper/1807.02851/) (2018)
- [Event Camera Calibration of Per-pixel Biased Contrast Threshold](https://mlsanity.com/paper/2012.09378/) (2020)
- [End-to-end Learning of Object Motion Estimation from Retinal Events for Event-based Object Tracking](https://mlsanity.com/paper/2002.05911/) (2020)
- [eCDT: Event Clustering for Simultaneous Feature Detection and Tracking-](https://mlsanity.com/paper/2207.09108/) (2022)
- [Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception](https://mlsanity.com/paper/2110.04988/) (2021)
- [Event-based Star Tracking via Multiresolution Progressive Hough Transforms](https://mlsanity.com/paper/1906.07866/) (2019)
- [A Linear Comb Filter for Event Flicker Removal](https://mlsanity.com/paper/2205.08090/) (2022)
- [Smart Visual Beacons with Asynchronous Optical Communications using Event Cameras](https://mlsanity.com/paper/2208.01710/) (2022)
- [Derivation of an EM algorithm for constrained and unconstrained multivariate autoregressive state-space (MARSS) models](https://mlsanity.com/paper/1302.3919/) (2013)

## Built on by (most cited first, 16 of 16)

- [ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras](https://mlsanity.com/paper/2410.09374/) (2024)
- [An Asynchronous Linear Filter Architecture for Hybrid Event-Frame Cameras](https://mlsanity.com/paper/2309.01159/) (2023)
- [Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application](https://mlsanity.com/paper/2503.22943/) (2025)
- [MATE: Motion-Augmented Temporal Consistency for Event-based Point Tracking](https://mlsanity.com/paper/2412.01300/) (2024)
- [EvTTC: An Event Camera Dataset for Time-to-Collision Estimation](https://mlsanity.com/paper/2412.05053/) (2024)
- [Asynchronous Multi-Object Tracking with an Event Camera](https://mlsanity.com/paper/2505.08126/) (2025)
- [TAPFormer: Robust Arbitrary Point Tracking via Transient Asynchronous Fusion of Frames and Events](https://mlsanity.com/paper/2603.04989/) (2026)
- [HelixTrack: Event-Based Tracking and RPM Estimation of Propeller-like Objects](https://mlsanity.com/paper/2603.09235/) (2026)
- [ASUMOT: Motion-Consistency-Based Asynchronous UAV Detection and Tracking with Event Cameras](https://mlsanity.com/paper/2607.11303/) (2026)
- [ODD-SEC: Onboard Drone Detection with a Spinning Event Camera](https://mlsanity.com/paper/2603.06265/) (2026)
- [Dual-Exposure Imaging with Events](https://mlsanity.com/paper/2604.10273/) (2026)
- [Equivariant Filter for High Performance Image Tracking using an Event Camera](https://mlsanity.com/paper/2607.09103/) (2026)
- [MotionDLO: Hybrid Event- and Frame-Based Tracking of Deformable Linear Objects](https://mlsanity.com/paper/2608.22398/) (2026)
- [Real-time Unsupervised Object Discovery from Asynchronous Event Streams](https://mlsanity.com/paper/2608.26644/) (2026)
- [REACT: A Fully Spiking State-Space Model for Real-Time Event-Driven Temporal Perception](https://mlsanity.com/paper/2609.19204/) (2026)
- [Asynchronous Tracking, Optical Communication and 3D Motion Capture using Event-based Sensors](https://mlsanity.com/paper/2610.04342/) (2026)

## Abstract (arXiv)

Event-based cameras are popular for tracking fast-moving objects due to their high temporal resolution, low latency, and high dynamic range. In this paper, we propose a novel algorithm for tracking event blobs using raw events asynchronously in real time. We introduce the concept of an event blob as a spatio-temporal likelihood of event occurrence where the conditional spatial likelihood is blob-like. Many real-world objects such as car headlights or any quickly moving foreground objects generate event blob data. The proposed algorithm uses a nearest neighbour classifier with a dynamic threshold criteria for data association coupled with an extended Kalman filter to track the event blob state. Our algorithm achieves highly accurate blob tracking, velocity estimation, and shape estimation even under challenging lighting conditions and high-speed motions (> 11000 pixels/s). The microsecond time resolution achieved means that the filter output can be used to derive secondary information such as time-to-contact or range estimation, that will enable applications to real-world problems such as collision avoidance in autonomous driving.