Robust Movement Detection in Outdoor Scenes

Yi Liu
M.Syst.Sc. student

Dr. Pierre Payeur
SITE, University of Ottawa


Natural Sciences and Engineering Research Council of Canada

Canadian Foundation for Innovation

Ontario Innovation Trust

Nowadays video surveillance systems are introduced in our world at a growing pace. Several systems have been proposed to monitor activity of people. Applications to traffic monitoring have also been developed. But while computer vision techniques are fairly reliable when they operate in a controlled environment, inside a building for example, several difficulties are still faced when cameras are installed in outdoor locations. This is especially true in nordic climate where lighting and weather conditions are far from being uniform throughout the year. Abrupt variations in lighting due to time-of-the-day and clouds movement, shadow effects, as well as changes in visibility resulting from fog, rain, snowfalls, or pollution, severely impede vision systems capability to detect and track activity.

This project involved the development of image-based movement detection systems that are robust to harsh outdoor conditions. Strategies have been elaborated and tested to monitor color map information in such a way that the vision system dynamically switches between various detection modes, according to the prevalent lighting and weather conditions. The implementation has been built around a traffic monitoring system meant to anticipate vehicles arrival at an intersection in order to optimize traffic lights control. Depending on the detection mode in operation, various features are monitored and shadow effects are compensated when potentially impeding the behavior of the system.

Tests conducted on real video sequences of outdoor traffic scenes in Ottawa, Canada, demonstrated the robustness of the proposed framework under various weather conditions and time-of-the-day operation.

Histrogram of a traffic scene

HS map over a rainy day

Traffic detection over a sunny day

Traffic detection over a snowy night

Related Publications

  • Y. Liu, P. Payeur, "Robust Image-Based Detection of Activity for Traffic Control", Canadian Journal of Electrical and Computer Engineering, vol. 28, no 2, pp. 63-67, April 2003. [pdf]

  • P. Payeur, Y. Liu, "Video Traffic Monitoring for Flow Optimization and Pollution Reduction", Proceedings of the 2nd IEEE/AEI International Workshop on Advanced Environmental Monitoring Technologies, pp. 53-58, Como, Italy, 24-25 July 2003. [pdf]

  • Y. Liu, P. Payeur, "Vision-Based Detection of Activity for Traffic Control", Proceedings of the Canadian Conference on Electrical and Computer Engineering (CCECE 2003), vol. 2, pp. 1347-1350, Montreal, QC, 4-7 May 2003. (Best Student Paper Award). [pdf]

© SMART Research Group, 2008