Linear-time connected-component labeling based on sequential local operations
Computer Vision and Image Understanding
A Real-time Computer Vision System for Measuring Traffic Parameters
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Discriminant Analysis Based Recursive Automatic Thresholding Approach for Image Segmentation
IEICE - Transactions on Information and Systems
A real-time object detecting and tracking system for outdoor night surveillance
Pattern Recognition
A Novel Background Extraction and Updating Algorithm for Vehicle Detection and Tracking
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
IEEE Transactions on Intelligent Transportation Systems
Traffic monitoring and accident detection at intersections
IEEE Transactions on Intelligent Transportation Systems
Image analysis and rule-based reasoning for a traffic monitoring system
IEEE Transactions on Intelligent Transportation Systems
Detection and classification of vehicles
IEEE Transactions on Intelligent Transportation Systems
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This study presents an effective system for detecting and tracking moving vehicles in nighttime traffic scene for traffic surveillance. The proposed method identifies vehicles based on detecting and locating vehicle headlights and taillights by using the techniques of image segmentation and pattern analysis. First, to effectively extract bright objects of interest, a fast bright-object segmentation process based on automatic multilevel histogram thresholding is applied on the nighttime road-scene images. This automatic multilevel thresholding approach can provide robustness and adaptability for the detection system to be operated well under various illumination conditions at night. The extracted bright objects are processed by a spatial clustering and tracking procedure by locating and analyzing the spatial and temporal features of vehicle light patterns, and then identifying and classifying the moving cars and motorbikes in the traffic scenes. Experimental results demonstrate that the proposed approach is feasible and effective for vehicle detection and identification in various nighttime environments for traffic surveillance.