Crowd-Auto - Locating Theft Vehicles Through Urban Crowdsensing - Baldi, Alessandro; Mota, Vinícius F. S.; Santos, Celso A. S. (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia), 2020)
Several cities around the world face security problems, such as vehicle theft. Locate and recover these vehicles are challengers for authorities. In smart cities, citizens can collaborate with authorities by sensing urban and environmental data, so-called crowdsensing. This work introduces Crowd-Auto, a crowdsensing approach that utilizes a crowded camera network from houses and commerce to identify vehicle plates, query on official databases and inform the authorities when stolen vehicles are identified. We've developed a prototype and demonstrated that Crowd-Auto is viable for allowing citizens to cooperate and improve security in cities.