Vision-based surveillance system for monitoring traffic conditions

With the rapid advancement of sensing technologies, it has been feasible to collect various types of traffic data such as traffic volume and travel times. Vision-based approach is one of the major scheme actively used for the automated traffic data collection, and continues to gain traction to a broader utilization. It collects video streams from cameras installed near roads, and processes the video streams frame by frame using image processing algorithms. The widely used algorithms include vehicle detection and vehicle tracking which recognize every vehicle in the camera view and track it in the consecutive frames. Vehicle counts and speed can be estimated from the detection and tracking results. Continuous efforts have been made for the performance improvement of the algorithms and for their effective applications. However, little research has been found on the application to the various view settings of highway CCTV cameras as well as the reliability of the speed estimation. This paper proposes a vision-based system that integrates vehicle detection, vehicle tracking, and field of view calibration algorithms to obtain vehicle counting data and to estimate individual vehicle speed. The proposed system is customized for the video streams collected from highway CCTVs which have various settings in terms of focus and view angles. The system detects and tracks every vehicle in the view unless it is occluded by other vehicles. It is also capable of handling occlusions that occurs frequently depending on the view angles. The system has been tested on the several different views including congested scenes. Vehicle counts and speed estimation results are compared to the manual counting and GPS data, respectively. The comparison signifies that the system has a high potential to extract reliable information about highway traffic conditions from highway CCTVs.