Document - Unsupervised vehicle counting via multiple camera domain adaptation

2020

Conference article Open Access

Unsupervised vehicle counting via multiple camera domain adaptation

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

Deep Learning Counting Objects Unsupervised Domain Adaptation Traffic Density Estimation

Monitoring vehicle flow in cities is a crucial issue to improve the urban environment and quality of life of citizens. Images are the best sensing modality to perceive and asses the flow of vehicles in large areas. Current technologies for vehicle counting in images hinge on large quantities of annotated data, preventing their scalability to city-scale as new cameras are added to the system. This is a recurrent problem when dealing with physical systems and a key research area in Machine Learning and AI. We propose and discuss a new methodology to design image-based vehicle density estimators with few labeled data via multiple camera domain adaptations.

Source: CEUR WORKSHOP PROCEEDINGS, pp. 1-4. Online Conference, 04 September, 2020

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BibTeX entry

@inproceedings{oai:it.cnr:prodotti:430982,
	title = {Unsupervised vehicle counting via multiple camera domain adaptation},
	author = {Ciampi L and Santiago C and Costeira Jp and Gennaro C and Amato G},
	booktitle = {CEUR WORKSHOP PROCEEDINGS, pp. 1-4. Online Conference, 04 September, 2020},
	year = {2020}
}