Multiview trajectory mapping using homography with lens distortion correction
Journal on Image and Video Processing - Regular
A low-cost solution for an integrated multisensor lane departure warning system
IEEE Transactions on Intelligent Transportation Systems
Road-condition recognition using 24-GHz automotive radar
IEEE Transactions on Intelligent Transportation Systems
Analysis of multiresolution-based fusion strategies for a dual infrared system
IEEE Transactions on Intelligent Transportation Systems
Multi-sensor IMM estimator for uncertain measurement
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
A general active-learning framework for on-road vehicle recognition and tracking
IEEE Transactions on Intelligent Transportation Systems
A channel awareness vehicle detector
IEEE Transactions on Intelligent Transportation Systems
Optimal use of plate-scanning resources for route flow estimation in traffic networks
IEEE Transactions on Intelligent Transportation Systems
Situation assessment for automatic lane-change maneuvers
IEEE Transactions on Intelligent Transportation Systems
Matrix tools for general observability analysis in traffic networks
IEEE Transactions on Intelligent Transportation Systems
Low-cost sensor to detect overtaking based on optical flow
Machine Vision and Applications
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This paper describes a vehicle detection system fusing radar and vision data. Radar data are used to locate areas of interest on images. Vehicle search in these areas is mainly based on vertical symmetry. All the vehicles found in different image areas are mixed together, and a series of filters is applied in order to delete false detections. In order to speed up and improve system performance, guard rail detection and a method to manage overlapping areas are also included. Both methods are explained and justified in this paper. The current algorithm analyzes images on a frame-by-frame basis without any temporal correlation. Two different statistics, namely: 1) frame based and 2) event based, are computed to evaluate vehicle detection efficiency, while guard rail detection efficiency is computed in terms of time savings and correct detection rates. Results and problems are discussed, and directions for future enhancements are provided