The world will witness a tremendous increase in the number of vehicles in the near future. Future traffic monitoring systems will therefore play an important role to improve the throughput and safety of roads. Current monitoring systems capture (usually vision-based) traffic data from a large sensory network; however, they require continuous human supervision which is extremely expensive.
In the proposed EVis research project we investigate the scientific and technological foundations for future autonomous traffic monitoring systems. Autonomy is achieved by a novel combination of three approaches: First, vision-based detection and classification methods are augmented by self-learning and scene adaptation mechanisms which will significantly reduce the effort of manual configuration. Second, visual data is fused with data from other sensors such as radar, infrared or inductive loop sensors. Sensor fusion helps to improve the robustness and confidence, to extend the spatial and temporal coverage as well as to reduce the ambiguity and uncertainty of the processed sensor data. Finally, the developed vision and fusion methods are implemented on a distributed embedded platform which makes them wider applicable and supports real-time operation.
Our autonomous traffic monitoring system will be evaluated using real world traffic data. The evaluation will be conducted in three different case studies: offline testing using recorded data, online testing on a traffic test site, and on a test installation on a public road.