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Article

Real-Time Multi-Camera Tracking for Vehicles in Congested, Low-Velocity Environments: A Case Study on Drive-Thru Scenarios

by
Carlos Gellida-Coutiño
1,*,
Reyes Rios-Cabrera
1,*,
Alan Maldonado-Ramirez
2 and
Anand Sanchez-Orta
1
1
Robotics and Advanced Manufacturing Division, Research Center for Advanced Studies (CINVESTAV), Industria Metalúrgica 1062, Parque Industrial Ramos Arizpe, Ramos Arizpe 25903, Mexico
2
Introid Inc., 199-Santa Susana Avenue, Saltillo 25297, Mexico
*
Authors to whom correspondence should be addressed.
Electronics 2025, 14(13), 2671; https://doi.org/10.3390/electronics14132671
Submission received: 30 May 2025 / Revised: 27 June 2025 / Accepted: 30 June 2025 / Published: 1 July 2025
(This article belongs to the Special Issue New Trends in Computer Vision and Image Processing)

Abstract

In this paper we propose a novel set of techniques for real-time Multi-Target Multi-Camera (MTMC) tracking of vehicles in congested, low speed environments, such as those of drive-thru scenarios, where metrics such as the number of vehicles, time of stay, and interactions between vehicles and staff are needed and must be highly accurate. Traditional methods of tracking based on Intersection over Union (IoU) and basic appearance features produce fragmented trajectories of misidentifications under these conditions. Furthermore, detectors, such as YOLO (You Only Look Once) architectures, exhibit different types of errors due to vehicle proximity, lane changes, and occlusions. Our methodology introduces a new tracker algorithm, Multi-Object Tracker based on Corner Displacement (MTCD), that improves the robustness against bounding box deformations by analysing corner displacement patterns and several other factors involved. The proposed solution was validated on real-world drive-thru footage, outperforming standard IoU-based trackers like Nvidia Discriminative Correlation Filter (NvDCF) tracker. By maintaining accurate cross-camera trajectories, our framework enables the extraction of critical operational metrics, including vehicle dwell times and person–vehicle interaction patterns, which are essential for optimizing service efficiency. This study tackles persistent tracking challenges in constrained environments, showcasing practical applications for real-world surveillance and logistics systems where precision is critical. The findings underscore the benefits of incorporating geometric resilience and delayed decision-making into MTMC architectures. Furthermore, our approach offers the advantage of seamless integration with existing camera infrastructure, eliminating the need for new deployments.
Keywords: multi-camera tracking; vehicle tracking; deep learning; congested spaces; low velocity; drive-thru; tracking errors; occlusion handling; metric plane; spatial analysis; multi-camera association; real-time systems; YOLOv7 multi-camera tracking; vehicle tracking; deep learning; congested spaces; low velocity; drive-thru; tracking errors; occlusion handling; metric plane; spatial analysis; multi-camera association; real-time systems; YOLOv7

Share and Cite

MDPI and ACS Style

Gellida-Coutiño, C.; Rios-Cabrera, R.; Maldonado-Ramirez, A.; Sanchez-Orta, A. Real-Time Multi-Camera Tracking for Vehicles in Congested, Low-Velocity Environments: A Case Study on Drive-Thru Scenarios. Electronics 2025, 14, 2671. https://doi.org/10.3390/electronics14132671

AMA Style

Gellida-Coutiño C, Rios-Cabrera R, Maldonado-Ramirez A, Sanchez-Orta A. Real-Time Multi-Camera Tracking for Vehicles in Congested, Low-Velocity Environments: A Case Study on Drive-Thru Scenarios. Electronics. 2025; 14(13):2671. https://doi.org/10.3390/electronics14132671

Chicago/Turabian Style

Gellida-Coutiño, Carlos, Reyes Rios-Cabrera, Alan Maldonado-Ramirez, and Anand Sanchez-Orta. 2025. "Real-Time Multi-Camera Tracking for Vehicles in Congested, Low-Velocity Environments: A Case Study on Drive-Thru Scenarios" Electronics 14, no. 13: 2671. https://doi.org/10.3390/electronics14132671

APA Style

Gellida-Coutiño, C., Rios-Cabrera, R., Maldonado-Ramirez, A., & Sanchez-Orta, A. (2025). Real-Time Multi-Camera Tracking for Vehicles in Congested, Low-Velocity Environments: A Case Study on Drive-Thru Scenarios. Electronics, 14(13), 2671. https://doi.org/10.3390/electronics14132671

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