# Event-based Object Detection and Tracking for Space Situational Awareness

arXiv: https://arxiv.org/abs/1911.08730 · HTML: https://mlsanity.com/paper/1911.08730/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 19 of 19)

- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [CMax-SLAM: Event-based Rotational-Motion Bundle Adjustment and SLAM System using Contrast Maximization](https://mlsanity.com/paper/2403.08119/) (2024)
- [SPADES: A Realistic Spacecraft Pose Estimation Dataset using Event Sensing](https://mlsanity.com/paper/2311.05310/) (2023)
- [Stereo Co-capture System for Recording and Tracking Fish with Frame- and Event Cameras](https://mlsanity.com/paper/2207.07332/) (2022)
- [JSTR: Joint Spatio-Temporal Reasoning for Event-based Moving Object Detection](https://mlsanity.com/paper/2403.07436/) (2024)
- [Segment Any Events via Weighted Adaptation of Pivotal Tokens](https://mlsanity.com/paper/2312.16222/) (2023)
- [Simultaneous Motion And Noise Estimation with Event Cameras](https://mlsanity.com/paper/2504.04029/) (2025)
- [On the Generation of a Synthetic Event-Based Vision Dataset for Navigation and Landing](https://mlsanity.com/paper/2308.00394/) (2023)
- [High Frequency, High Accuracy Pointing onboard Nanosats using Neuromorphic Event Sensing and Piezoelectric Actuation](https://mlsanity.com/paper/2309.01361/) (2023)
- [Event-based Structure-from-Orbit](https://mlsanity.com/paper/2405.06216/) (2024)
- [Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: a Review](https://mlsanity.com/paper/2504.08588/) (2025)
- [Efficient Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGA](https://mlsanity.com/paper/2305.19468/) (2023)
- [LAND: A Longitudinal Analysis of Neuromorphic Datasets](https://mlsanity.com/paper/2602.15973/) (2026)
- [Efficient Onboard Spacecraft Pose Estimation with Event Cameras and Neuromorphic Hardware](https://mlsanity.com/paper/2604.04117/) (2026)
- [Tracking Particles Ejected From Active Asteroid Bennu With Event-Based Vision](https://mlsanity.com/paper/2309.06819/) (2023)
- [Mixing Data-driven and Geometric Models for Satellite Docking Port State Estimation using an RGB or Event Camera](https://mlsanity.com/paper/2409.15581/) (2024)
- [An efficient neuromorphic approach for collision avoidance combining Stack-CNN with event cameras](https://mlsanity.com/paper/2506.16436/) (2025)
- [FPGA-Accelerated Neuromorphic Vision System for Real-Time Orbital Object Detection](https://mlsanity.com/paper/2606.20727/) (2026)
- [Neuromorphic Object Detection: An In-Depth Study and Future Directions](https://mlsanity.com/paper/2607.23576/) (2026)

## Abstract (arXiv)

In this work, we present optical space imaging using an unconventional yet promising class of imaging devices known as neuromorphic event-based sensors. These devices, which are modeled on the human retina, do not operate with frames, but rather generate asynchronous streams of events in response to changes in log-illumination at each pixel. These devices are therefore extremely fast, do not have fixed exposure times, allow for imaging whilst the device is moving and enable low power space imaging during daytime as well as night without modification of the sensors. Recorded at multiple remote sites, we present the first event-based space imaging dataset including recordings from multiple event-based sensors from multiple providers, greatly lowering the barrier to entry for other researchers given the scarcity of such sensors and the expertise required to operate them. The dataset contains 236 separate recordings and 572 labeled resident space objects. The event-based imaging paradigm presents unique opportunities and challenges motivating the development of specialized event-based algorithms that can perform tasks such as detection and tracking in an event-based manner. Here we examine a range of such event-based algorithms for detection and tracking. The presented methods are designed specifically for space situational awareness applications and are evaluated in terms of accuracy and speed and suitability for implementation in neuromorphic hardware on remote or space-based imaging platforms.