Crowdsensing and Image Processing as a Method for Analysis and Population Count Based on the Classification and Validation of Multimedia

Autor:	Alexander Mejía, Luis Tello-Oquendo, Marcelo Olalla, Freddy Tapia, Bryan Oscullo
Rok vydání:	2020
Předmět:	education.field_of_study Artificial neural network Computer science Control (management) Population Data validation Image processing computer.software_genre Quantities of information Crowdsensing Urban planning Data mining education computer
Zdroj:	Communications in Computer and Information Science ISBN: 9783030425197
DOI:	10.1007/978-3-030-42520-3_46
Popis:	The growth of cities and the advancement of technology demands the development of solutions that can help and reduce emerging problems. One of these problems is the control of the population and urban planning based on population count, flow, and density analysis. This paper proposes an approach that could help cities to gather population data in a contextualized manner with the usage of localization, sensor, and weather data. It is based on a method to collect vast quantities of information and a way to validate and analyze gathered data. The data recollection is achieved through crowdsensing and smartphones. Validation and analysis are made with cloud-based image analysis and neural networks. Results show the usefulness and effectiveness of the proposed solution; also, some considerations are presented with the proposal.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9ff970ba7def03f541a80924a01b7739 https://doi.org/10.1007/978-3-030-42520-3_46 Zobrazit plný text záznamu