Real-Time Urban Traffic Monitoring System Based on Edge Computing: Vehicle Speed Estimation via Homography and Automatic License Plate Recognition on Jetson Orin NX for Andean Urban Corridors

This paper presents a distributed edge computing system for real-time urban traffic monitoring that integrates vehicle speed estimation and automatic license plate recognition (ALPR) on NVIDIA Jetson Orin NX devices. The system addresses the need for low-cost, automated traffic enforcement in developing urban environments where certified radar-based speed measurement infrastructure is not available. The proposed architecture consists of two independent processing nodes. The first node performs speed estimation using an IMOU aerial camera, YOLO26n-based vehicle detection, ByteTrack multi-object tracking, perspective homography transformation, and Kalman filtering for instantaneous speed computation. The second node performs license plate recognition employing a frontal camera, YOLO26n detection, and a Compact Convolutional Transformer (CCT) OCR model for real-time plate reading. Both subsystems are deployed on Jetson Orin NX modules with TensorRT FP16-optimized models, sustaining 20.0–22.3 FPS for speed estimation and 29–36 FPS for license plate recognition. The system incorporates the Peruvian traffic regulation (DS 025-2021-MTC) for automated violation classification, including speeding offenses (code M20) with the 5 km/h camera tolerance threshold introduced by DS 011-2026-MTC. Field validation was conducted at Jr. Carlos F. Vivanco, Ayacucho, Peru (2719–2725 m a.s.l.). Speed estimation was validated in a 40-pass nocturnal campaign against 1 Hz GNSS Doppler velocity spanning 16.9–48.8 km/h, a range that covers both the 30 km/h statutory limit and the 35 km/h enforcement tolerance. The configuration deployed during the campaign, which smooths the metric foot-point with a constant-velocity Kalman filter, attained a mean absolute error of 3.19 km/h at the enforcement line. The residual was identified as the steady-state lag of that motion model under vehicle acceleration, with which it correlates at r = −0.727; replacing it with a constant-acceleration model reduces the error to 1.32 km/h under leave-one-pass-out cross-validation and removes the dependence on acceleration entirely (r = +0.003). The improvement was verified to transfer to the Jetson deployment node. Two earlier campaigns are retained for completeness because they identified deployment failure modes—an RTSP stream resolution renegotiation and a speed-display defect affecting vehicles stopped beyond the measurement zone—that are reported as engineering contributions in their own right. A comprehensive model development study, comprising six training experiments (M1–M6) across three architectures (YOLOv8m-seg, YOLO11m-seg, YOLO26-seg/det), documents the methodological transition from instance segmentation to bounding-box detection driven by edge computing latency constraints. The results demonstrate the feasibility of deploying a traffic enforcement prototype using commercial edge devices and IP camera infrastructure, offering a scalable and cost-effective solution for smart city applications in developing regions.