Optimizing Deep Learning Models for Edge Devices in Threat Detection for Computer Vision Applications in Smart Cities and Retail

During the internship, the selected candidate will focus on developing edge computing solutions that can recognize and alert the relevant personnel in real-time in case of potential security threats (e.g. theft, robbery) and safety issues (e.g. employee accidental falls). This would help retailers to prevent or respond quickly to incidents, reducing losses and improving safety for customers and employees. The internship will have a set of milestones which will involve developing the key building blocks for such an application, including exploring algorithms and techniques for object detection, motion detection, face recognition, and activity recognition. The models will be trained on high-performance computing resources and tested on edge compute devices. The intern will also be responsible for real-time testing of the developed edge computing solution, executing proof of concepts, and deploying the AI models on the edge computing hardware platform such as cameras.
The internship will provide J-Squared Technologies with valuable research that can be used to continue building optimized edge-computing solutions. Additionally, the project will help J-Squared Technologies establish an AI/ML research team that can work on the latest state-of-the-art artificial intelligence techniques to create solutions that benefit society.

Faculty Supervisor:

Babak Taati

Student:

Partner:

J-Squared Technologies

Discipline:

Computer science

Sector:

Manufacturing

University:

University of Toronto

Program: