, Assistant Professor, UMD-Electrical Engineering
Automatic traffic data collection can significantly save labor and costs compared to manual data collection. However, automatic traffic data collection has been a challenge in intelligent transportation systems (ITS). To be practical and useful, an automatic traffic data collection system must derive traffic data with reasonable accuracy compared to a manual approach. This project presents the development of a multiple-camera tracking system for accurate traffic performance measurements at intersections. The tracking system sets up multiple cameras to record videos for an intersection. Compared to the traditional single-camera-based tracking system, the multiple-camera one can take advantage of significantly overlapped views of the same traffic scene such that the notorious vehicle occlusion problem is alleviated. Also, multiple cameras provide more evidence of the same vehicle, which allows more robust tracking of the vehicle. The developed system uses three processing modules. First, the camera is calibrated for the traffic scene of interest and a calibration algorithm is developed for multiple cameras at an intersection. Second, the system tracks vehicles from the multiple videos by using powerful imaging processing techniques and tracking algorithms. Finally, the resulting vehicle trajectories from vehicle tracking are analyzed to extract the interested traffic data such as vehicle volume, travel time, rejected gaps, and accepted gaps. Practical tests of the developed system focus on vehicle counts and achieve reasonable accuracy.
- Project number: 2012016
- Start date: 07/2011
- Project status: Completed
- Research area: Transportation Safety and Traffic Flow
Data and modeling