Intra-grid location estimation of smartphone videos using ENF extraction and an improved super-pixel technique

Ericmoore Ngharamike; Li-Minn Ang; Mingzhong Wang; Kah Phooi Seng

doi:10.1038/s41598-025-89184-w

Back

Intra-grid location estimation of smartphone videos using ENF extraction and an improved super-pixel technique

Journal article

Open access

Peer reviewed

Intra-grid location estimation of smartphone videos using ENF extraction and an improved super-pixel technique

Ericmoore Ngharamike, Li-Minn Ang, Mingzhong Wang and Kah Phooi Seng

Scientific Reports, Vol.15(1), pp.1-19

2025

DOI: https://doi.org/10.1038/s41598-025-89184-w

PMCID: PMC12137870

PMID: 40467992

Files and links (1)

pdf

s41598-025-89184-w5.07 MBDownload View

Published Version Open Access CC BY-NC-ND V4.0

Abstract

engineering

mathematics and computing

Determining the location of a video is crucial for law enforcement and national security. While outdoor videos can use landmarks or geolocation techniques, indoor videos present a greater challenge due to the lack of visible landmarks. Recently, researchers have explored using the electrical network frequency (ENF), naturally embedded in audio and video sources, for this purpose. Most studies have focused on extracting ENF from different electrical grids (inter-grid scenarios), where ENF from one grid differs significantly from another. However, recent research shows that ENF recordings from different locations within the same grid (intra-grid scenarios) have minor variations that can help estimate the video's location. Previous work used ENF recordings directly from electrical mains. This paper proposes a technique to extract ENF from smartphone videos in intra-grid scenarios using an improved super-pixel approach. Unlike direct ENF extraction from electrical mains, extracting ENF from smartphone videos is challenging due to sensor noise and aliasing effects. Our improved super-pixel technique mitigates these issues. We validated our approach with real-world smartphone video recordings from various locations within the Australian Eastern grid. By comparing the extracted ENF signatures with ground truth ENF data from anchor locations provided by a power supplier, we demonstrated the ability to estimate the location of smartphone videos within the same electrical grid. To our knowledge, this is the first practical demonstration of location estimation for smartphone videos in an intra-grid scenario.

Details

Title: Intra-grid location estimation of smartphone videos using ENF extraction and an improved super-pixel technique
Authors: Ericmoore Ngharamike (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Li-Minn Ang - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Mingzhong Wang - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Kah Phooi Seng - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Publication details: Scientific Reports, Vol.15(1), pp.1-19
Publisher: Nature Publishing Group
Date published: 2025
DOI: 10.1038/s41598-025-89184-w
ISSN: 2045-2322
PMID: 40467992; PMC12137870
Copyright note: This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Data Availability: The dataset generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Organisation Unit: Healthy Ageing Research Cluster; School of Science, Technology and Engineering; Engage Research Lab
Language: English
Record Identifier: 991133605102621
Output Type: Journal article

Metrics

1 File views/ downloads

60 Record Views

1 Times Cited - Web of Science

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Web Of Science research areas: Engineering, Electrical & Electronic