Research Project


Prolonged daily periods of road traffic congestion waste time, and money, and degrade both the environment and our quality of life. In Egypt, the problem is significant with severe traffic delays and high accident rates leading to devastating effects on economic growth and challenging any progression towards sustainable development. Conventional traffic management strategies have traditionally focused on the expansion of the transportation network capacity such as building new highways and bridges. Capacity expansion, however, failed to keep pace with sharp increases in demand, resulting in widely spreading congestion and raising concerns about the long-term sustainability of this approach. As advances in information and communications technologies continue to revolutionize all aspects of our lives, real-time control of our traffic network becomes more viable. The implementation of such systems is steadily becoming a reality that will reshape the way people, vehicles, and roads interact through technology.

The TraffiSense-Pro project aims at completing the development of an integrated system for road traffic management. The proposed system builds upon an existing prototype and will facilitate the key traffic management tasks of (a) traffic network monitoring to sense and enumerate the activities of vehicles and detect law violations; (b) transformation of raw traffic data into useful information such as congestion levels and traffic stream parameters; and (c) provision of real-time traffic information to traffic management and law enforcement authorities as well as traffic network commercial end users.


Outcome: Publications

  • Salaheldin, S. Maher, M. ElHelw, “Robust Real-Time Tracking with Diverse Ensembles and Random Projections”, International Conference on Computer Vision (ICCV), Workshop on Visual Object Tracking (VOT), Sydney, Australia, 2013.

  • S. Elkerdawy, A. Eldesokey, A. Salaheldin, M. ElHelw, "Scale-Adaptive Object Tracking with Diverse Ensembles", Advances in Visual Computing, Springer Lecture Notes in Computer Science, 2014.

  • S. Elkerdawy, A. Salaheldin, M. ElHelw, "Vision-based scale-adaptive vehicle detection and tracking for intelligent traffic monitoring", IEEE International Conference on Robotics and Biomimetics (ROBIO), Bali, Indonesia, 2014.

  • M. Elhelw, S. Maher, A. Salaheldin, Visual Scale-adaptive Tracking For Smart Traffic Monitoring, Qatar Annual Research Conference, Doha, Qatar, 2014. (Poster)

  • R. MAged, M. ElHelw, TraffiSense: An Integrated Visual Traffic Monitoring System, IntelliSys 2015, London, UK.