Anomaly Detection in Land Vehicle Traffic Activity

This projects objective is to develop a capability to detect and describe anomalous situations in ground vehicle traffic. Anomalous situations are described as substantial/important changes from the traffic frequently observed for a particular route and/or time. In this sense, anomaly can be quantitatively measured by the degree of predictability of current traffic given historical observations. In the use case of interest, information from traffic will be captured from a GMTI sensor performing recurrent surveillances (1-3 hours per day, multiple days per week) over the same area. By developing such capability, Thales wishes to create a new service offer (based on existing but still un tapped historical data): a decision support capability for GMTI analysts that will draw their attention on suspicious activities that would normally be unnoticed. This project does not deal with raw data processing or tracking problems, but uses vehicles tracks as input data.

Faculty Supervisor:

Lijun Sun

Student:

Xudong Wang

Partner:

Thales Canada Inc.

Discipline:

Engineering - civil

Sector:

Information and communications technologies

University:

Program: