Document - Traffic density estimation via unsupervised domain adaptation

2021

Conference article Open Access

Traffic density estimation via unsupervised domain adaptation

Ciampi L, Santiago C, Costeira Jp, Gennaro C, Amato G

Deep Learning Counting objects Unsupervised domain adaptation Traffic density estimation Synthetic dataset

Monitoring traffic flows in cities is crucial to improve urban mobility, and images are the best sensing modality to perceive and assess the flow of vehicles in large areas. However, current machine learning-based technologies using images hinge on large quantities of annotated data, preventing their scalability to city-scale as new cameras are added to the system. We propose a new methodology to design image-based vehicle density estimators with few labeled data via an unsupervised domain adaptation technique.

Source: CEUR WORKSHOP PROCEEDINGS, pp. 442-449. Pizzo Calabro, Italy, 05-09/09/2021

Back to previous page

Cite as

BibTeX entry

@inproceedings{oai:it.cnr:prodotti:461455,
	title = {Traffic density estimation via unsupervised domain adaptation},
	author = {Ciampi L and Santiago C and Costeira Jp and Gennaro C and Amato G},
	booktitle = {CEUR WORKSHOP PROCEEDINGS, pp. 442-449. Pizzo Calabro, Italy, 05-09/09/2021},
	year = {2021}
}