Video Analytics and RF People Sensing for Smart City and New Retail
Project Description

To research and develop a state-of-the-art RF (Wi-Fi and ibeacon) and video analytics technologies for pervasive wireless tracking and people sensing. Students involved will actively work with my R&D team and industry to deploy the technology. Machine learning techniques will be involved to enable new retail, smart city and new applications.

Website:
http://mwnet.cse.ust.hk/smartap
and
http://www.yfisoft.com

Supervisor
CHAN Gary Shueng Han
Quota
5
Course type
UROP1000
UROP1100
UROP2100
UROP3100
UROP4100
Applicant's Roles

Students will research and develop various advanced RF (Wi-Fi and ibeacon) and video analytics technologies, their algorithms and protocols, to support user tracking, people sensing, roaming, channel and routing assignment, etc. This includes Wi-Fi router design, ibeacon sensing, camera innovations, and data/video analytics for large-scale deployment. They will help on research, prototyping, simulation, and experimental trials. Students in the project will actively involved in industrial deployment based on our research results to enable new retail, promote smart city and create new market opportunities. Documentations in the form of patent, papers, and presentations will also be involved.

Applicant's Learning Objectives

Ability to understand what a wireless router is, how ibeacon works, how smart camera is designed, how video and RF play a role to sense users, data mining and user analytics, etc. Ability to do network programming, network administration, video analytics and protocol design. Ability to write network programs and Linux codes.

Complexity of the project
Moderate