| Full text | |
| 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 |