This paper presents a computer vision algorithm that detects, by analyzing lane marking detection results, stop-lines and tracks, using an unscented Kalman filter, the detected stop-line over time
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This paper presents a computer vision algorithm that detects, by analyzing lanemarking detection results, stop-lines and tracks, using an unscented Kalman filter, the detected stop-line over time. To detect lateral and longitudinal lane-markings, our method applies a spatial filter emphasizing the intensity contrast between lanemarking pixels and their neighboring pixels. We then examine the detected lanemarkings to identify perpendicular, geometry layouts between longitudinal and lateral lane-markings for stop-line detection. To provide reliable stop-line recognition, we developed an unscented Kalman filter to track the detected stop-line over frames. Through the testings with real-world, busy urban street videos, our method demonstrated promising results, in terms of the accuracy of the initial detection accuracy and the reliability of the tracking.
Abstract
Abstract Increasing congestion on freeways and problems associated with existing detectors have spawned an interest in new vehicle detection technologies such as video image processing. Existing commercial image processing systems work well in free-¯owing trac, but the systems have diculties with congestion, shadows and lighting transitions. These problems stem from vehicles partially occluding one another and the fact that vehicles appear dierently under various lighting conditions. We are developing a feature-based tracking system for detecting vehicles under these challenging conditions. Instead of tracking entire vehicles, vehicle features are tracked to make the system robust to partial occlusion. The system is fully functional under changing lighting conditions because the most salient features at the given moment are tracked. After the features exit the tracking region, they are grouped into discrete vehicles using a common motion constraint. The groups represent individual vehicle trajectories which can be used to measure traditional trac parameters as well as new metrics suitable for improved automated surveillance. This paper describes the issues associated with feature based tracking, presents the real-time implementation of a prototype system, and the performance of the system on a large data set. # 1999 Elsevier Science Ltd. All rights reserved