Real-time path identification and visualization in indoor environment using image analysis and deep learning techniques

The number of people requiring indoor navigational assistance (e.g., blind, elderly people) is increasing rapidly all over the world. People often get lost in complex places like hospital buildings. Unfortunately, the GPS service is not available indoor. The main objective of this project is to develop a robust and fast navigational technique based on continuous real–time image analysis and deep learning techniques. The proposed technique, namely NavNet, can be implemented through cell phone or an electronic display to guide the user from a starting point to the destination within an indoor environment. Although the primary intended application is to help people finding navigation path, the developed techniques can be also be extended to other applications such as robotics and disaster management. Clinisys develops a variety of secure, scalable and user-friendly e-Healthcare solutions and medical devices for the healthcare industry and patient population. The proposed project ideation emerged from discussions with several stakeholders e.g., the rehab patients at Glenrose Rehabilitation hospital. The proposed navigational software will be an important addition into the Clinisys Patient Portal.

Faculty Supervisor:

Mrinal Mandal

Student:

Partner:

Clinisys EMR Inc.

Discipline:

Engineering

Sector:

Information and Communications Technology; Artificial Intelligence; Transportation (excluding aerospace)

University:

University of Alberta

Program: