Document Type
Article
Publication Date
8-5-2025
Abstract
Event-based vision sensors (EVSs), often referred to as neuromorphic cameras, operate by responding to changes in brightness on a pixel-by-pixel basis. In contrast, traditional framing cameras employ some fixed sampling interval where integrated intensity is read off the entire focal plane at once. Similar to traditional cameras, EVSs can suffer loss of sensitivity through scenes with high intensity and dynamic clutter, reducing the ability to see points of interest through traditional event processing means. This paper describes a method to reduce the negative impacts of these types of EVS clutter and enable more robust target detection through the use of individual pixel frequency analysis, background suppression, and statistical filtering. Additionally, issues found in normal frequency analysis such as phase differences between sources, aliasing, and spectral leakage are less relevant in this method. The statistical filtering simply determines what pixels have significant frequency content after the background suppression instead of focusing on the actual frequencies in the scene. Initial testing on simulated data demonstrates a proof of concept for this method, which reduces artificial scene noise and enables improved target detection.
Source Publication
Signals (eISSN 2624-6120)
Recommended Citation
Johnston, W.; Young, S.; Howe, D.; Oliver, R.; Theis, Z.; McReynolds, B.; Dexter, M. Method for Target Detection in a High Noise Environment Through Frequency Analysis Using an Event-Based Vision Sensor. Signals 2025, 6, 39. https://doi.org/10.3390/signals6030039
Comments
© 2025 by the authors. Licensee MDPI, Basel, Switzerland.
This article is published by MDPI, licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Sourced from the published version of record cited below.
Funding note: This research was funded by the Space Vehicles Directorate, Air Force Research Laboratory, 3550 Aberdeen Ave SE, Albuquerque, NM 87117, USA. Contract number: 01705400005D.