| dc.description.abstract | There has been a growth in demand for advancing algorithms in surveillance applications concerning moving vehicles where analysis of traffic has a potential application to security, traffic management (congestion and accident detection), speed measurement, car counting and statistics, as well as turning movement at intersections. This research focuses on multiple-vehicle detection, recognition, and tracking in urban environments based on video sequences obtained from a single CCD camera mounted on a pole at urban highways and crossroads. The proposed system integrates several modules including segmentation, object detection, object recognition and classification, and tracking. Background segmentation, based on Gaussian Mixture models, is used to extract moving objects from images using the respective foreground object information such as location, size, and color distribution. To recognize vehicles, a 3D polyhedral car model described by a set of parameters is built and mapped to the 2D edge information attained from the video sequence. The matching process is then used to classify the foreground object obtained into vehicles and non-vehicles. The output from the recognition model is used in tracking multiple cars based on a deterministic data association method that takes place between consecutive frame information.
The multiple-vehicle surveillance system developed in this thesis, based on integrating different modules, provides a novel approach for vehicle monitoring. Furthermore, the system makes use of minimal a priori knowledge about vehicle location, size, type, numbers, and pathways. The system implemented in this work functions well under various camera perspectives, background clutter, vehicle viewpoints, road types, scale changes, image noise, image resolutions, and lighting conditions. | en |
