Data-driven city model representation and analysis using visual and non-visual information

• Grant number: 16/12077-6
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: October 01, 2016
• End date: September 30, 2017
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Roberto Marcondes Cesar Junior
• Grantee: Eric Keiji Tokuda
• Supervisor: Jose Claudio Teixeira e Silva Junior
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Institution abroad: New York University, United States
• Associated to the scholarship: 14/24918-0 - Weakly supervised learning for face and person attributes detection, BP.DR

Abstract

Thanks to the advent and widespread of new technologies, data are generated all the time, in many forms and from many sources. In particular, cities daily generate a huge amount of data such as from surveillance cameras, demographic statistics and tax revenues. Such data can be efficiently employed to build a city model, where semantic information can be extracted and future events can be predicted. We propose to develop a data-driven approach for representing important city information. Relevant data will be extracted from visual and non-visual data sources. Visual sources include maps, images, videos from urban areas. Non-visual sources include socio-economics statistics and related data regarding a certain urban environment. Computer Vision and Pattern Recognition algorithms are going to be employed to analyze the data in order to extract relevant information. This document is based on a common project for Eric Keiji Tokuda (PhD proc. 14/24918-0) and Gabriel Augusto Bianchi Azevedo Ferreira (Scientific Initiation proc. 15/03475-5). Specific activities for each student are indicated. Particularly, this project aims at people detection in urban environment. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

TOKUDA, ERIC K.; FERREIRA, GABRIEL B. A.; SILVA, CLAUDIO; CESAR-, ROBERTO M., JR.; IEEE. A NOVEL SEMI-SUPERVISED DETECTION APPROACH WITH WEAK ANNOTATION. 2018 IEEE SOUTHWEST SYMPOSIUM ON IMAGE ANALYSIS AND INTERPRETATION (SSIAI), v. N/A, p. 4-pg., 2018-01-01. (15/22308-2, 16/12077-6, 14/24918-0, 15/03475-5)