Autonomous UAV Implementation for Facial Recognition and Tracking in GPS-Denied Environments

Autonomous UAV Implementation for Facial Recognition and Tracking in GPS-Denied Environments

Surveillance with facial recognition holds immense potential as a technological tool for combating crime in Latin American countries. However, the limitations of fixed cameras in covering wide areas and tracking suspects as the evaded recognitions systems pose significant challenges. To address thes...

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Bibliographic Details
Published in:IEEE access Vol. 12; pp. 119464 - 119487
Main Authors: Ollachica, Diego A. Herrera, Asante, Bismark K. Asiedu, Imamura, Hiroki
Format: Journal Article
Language:English
Published: IEEE 2024
Subjects:
Autonomous aerial vehicles
autonomous flight
Cameras
deep learning
Drones
Embedded systems
Face detection
Face recognition
facial recognition
Object tracking
Unmanned autonomous vehicles (UAV)
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Summary:Surveillance with facial recognition holds immense potential as a technological tool for combating crime in Latin American countries. However, the limitations of fixed cameras in covering wide areas and tracking suspects as the evaded recognitions systems pose significant challenges. To address these limitations, we propose a facial recognition system designed to recognize faces of suspected individuals with criminal backgrounds and missing persons. Our solution combines facial recognition technology with a custom-built unmanned aerial vehicle (UAV) for the identification and tracking of targeted persons listed in a database for crimes. We utilize the inception v2 model to deploy a Siamese network on the Jetson TX2 platform for facial recognition. Additionally, we introduce a novel tracking algorithm to track suspected individuals in the event of evasion. During field test experiments, our system demonstrated strong performance in facial recognition across three different environments: stationary, indoor flight, and outdoor flight. The accuracy of our system was 94.45% for recognizing along with our tracking algorithms. An improvement of 1.5% in recognition and better tracking approach for surveillance. This indicates the versatility and effectiveness of our solution in various operational scenarios, enhancing its potential for crime prevention and law enforcement efforts in Latin American countries.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3447899