Computer Vision System Proposal using Re-Identification techniques to improve Multi-Camera Vehicle Tracking Management in Trujillo, Peru

Abstract

This research work described the issues regarding vehicle monitoring management in Trujillo, Peru, and aimed to propose a computer vision system to optimize multi-camera tracking in the year 2026. The study was descriptive-propositional with a quantitative approach; a questionnaire was applied to a non-probabilistic sample of 50 control center operators. The diagnosis revealed critical deficiencies in current operations, highlighting high discontinuity in inter-camera tracking (4.2) and visual fatigue in screen comparison (4.0), demonstrating the inefficiency of manual processes. In response, the UrbanSight technical proposal was designed, integrating the YOLOv11 model for vehicle detection and optimizing the DeepSORT algorithm through Re-Identification (ReID) techniques trained with the VeRi-776 dataset. It was concluded that this technological integration allows maintaining vehicle identity persistence across video surveillance networks, representing a viable solution to automate traceability and reduce reliance on the human factor.