Reidentificação baseada em filtro de correlação discriminativo para rastreamento de múltiplos objetos em câmeras de videomonitoramento

Abstract

This study aims to develop, test, and analyze the use of discriminative correlation filter as a module for object re-identification, integrated with multiple object tracking for use in surveillance cameras with a focus on real-time processing. The study is set in the context of smart cities and Intelligent Transportation Systems (ITS), where object re identification and tracking are fundamental for the creation of advanced technologies. The adopted methodology includes the implementation of a modified discriminative correlation filter for the re-identification task, followed by tests to evaluate the algorithm’s performance in challenging scenarios present in widely recognized datasets in computer vision challenges. The results showed that the proposed correlation filter approaches the accuracy of neural network-based approaches without the need for prior training for specific contexts. Therefore, we may conclude that the integration of this re-identification module with multi-object tracking offers a balanced solution to improve tracking accuracy at a lower computational cost compared to neural networks, contributing to the advancement of technologies in smart cities and ITS