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

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Author(s):	Lessa Ribeiro, Matheus Vieira ; Aching Samatelo, Jorge Leonid ; Cetertich Bazzan, Ana Lucia Total Authors: 3
Document type:	Journal article
Source:	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS; v. 23, n. 4, p. 5-pg., 2022-04-01.
Abstract
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)

FAPESP's process:	15/24423-3 - 2UEI -- internet 2.0 and mobility internet as heterogeneous data sources for smart cities
Grantee:	Ana Lúcia Cetertich Bazzan
Support Opportunities:	Regular Research Grants


FAPESP's process:	18/05150-4 - 2 UEI Internet 2.0 and boarded by vehicle as heterogeneous sources data on intelligent cities
Grantee:	Matheus Vieira Lessa Ribeiro
Support Opportunities:	Scholarships in Brazil - Technical Training Program - Technical Training