A New Microscopic Approach to Traffic Flow Classification Using a Convolutional Neural Network Object Detector and a Multi-Tracker Algorithm

Autor(es)

Lessa Ribeiro, Matheus Vieira ; Aching Samatelo, Jorge Leonid ; Cetertich Bazzan, Ana Lucia

• Tipo de documento: Artigo Científico
• Fonte: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS; v. 23, n. 4, p. 5-pg., 2022-04-01.

Resumo

Traffic congestion is a significant issue in urban areas and can cause adverse effects. In this paper. our proposal categorizes the traffic activity from video through three steps: vehicle monitoring, feature extraction, and classification. The vehicle monitoring step comprises an object detector based on a convolutional neural network and multi-object tracker. The feature extraction step uses information related to each detected vehicle, in various points of the road, to represent the traffic condition through three features: density, flow, and velocity. We tested on the UCSD dataset and achieved the best performance with 98.82% of accuracy, which outperformed the state-of-the-art methods. (AU)

• Processo FAPESP: 15/24423-3 - 2 UEI internet 2.0 e embarcada em veículos como fontes heterogêneas de dados em cidades inteligentes
• Beneficiário: Ana Lúcia Cetertich Bazzan
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 18/05150-4 - 2 UEI internet 2.0 e embarcada em veículos como fontes heterogêneas de dados em cidades inteligentes
• Beneficiário: Matheus Vieira Lessa Ribeiro
• Modalidade de apoio: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico