Unsupervised vehicle counting via multiple camera domain adaptation
By Luca Ciampi, Carlos Santiago, João Paulo Costeira, Claudio Gennaro, Giuseppe Amato. 2020
10 citations recorded by the source corpus.
Monitoring vehicle flows in cities is crucial to improve the urban environment and quality of life of citizens. Images are the best sensing modality to perceive and assess 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.
Subfields: Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning
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In Computer Vision and Pattern Recognition
- Luca Ciampi
- Giuseppe Amato
- Claudio Gennaro
- Carlos Santiago
- João Paulo Costeira
- Fabrizio Falchi
- Nicola Messina
- Lourdes de Agapito
In Representation & Self-Supervised Learning
Citations in the corpus
- Domain Adaptation for Traffic Density Estimation (2021, methodology): “To the best of our knowledge, this work is the first that employs UDA to the counting task, extending the very preliminary results obtained in (Ciampi et al., 2020b), where a similar approach was exploited in just one limited scenario.”
- Virtual to Real Adaptation of Pedestrian Detectors (2020, background): “For example, authors in [29] and in [30] proposed two unsupervised domain adaptation solutions for the counting task and the segmentation task, respectively, taking advantage of the output space.”