Use of mixed reality for urban security perception

• Grant number: 24/05760-8
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Start date: May 01, 2024
• End date: August 31, 2027
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Agreement: MCTI/MC
• Principal Investigator: Jorge Luis Poco Medina
• Grantee: Victor Andres de La Puente Ancco
• Host Institution: Escola de Matemática Aplicada (EMAp). Fundação Getúlio Vargas (FGV)
• Associated research grant: 21/07012-0 - Data-driven intelligence for urban crime analysis and perception, AP.JP

Abstract

The perception of urban security is closely related to urban crime. It is of utmost importance to understand the perception of security in urban contexts since residents' quality of life could be improved by understanding its factors. Currently, urban crime affects the entire world; in some cities, it is more prevalent than in others, and neighbors are always the most affected. That is why great efforts exist to understand the causes of this sociological phenomenon. The most interested organizations are the governments and police of each country. The current state of the art in criminal matters determines the causal factors that allow a person to commit a crime: poverty, lack of food, and environment. There is a clear need for advanced and adaptable methods to find these causal factors and better understand the crime phenomenon. Mixed reality is a technology that merges elements of the real and virtual world to create immersive and collaborative experiences. This project aims to use mixed reality to analyze the perception of urban safety. Leveraging the potential of mixed reality, we seek to generate new data, such as a person's gestures and facial expressions when viewing an image of a street, to understand the most important variables that influence whether one street is more dangerous than another. A subsequent phase involves integrating a system with mixed reality glasses to use it in real-time and predict whether a street is risky. (AU)