Abstract | ||
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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 |
Year | DOI | Venue |
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2007 | 10.1109/TITS.2006.888597 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
vision data fusion,radar data,false detection,guard rail detection,different image area,different statistic,vehicle search,correct detection rate,vehicle detection system fusing,vehicle detection efficiency,guard rail detection efficiency,radar images,algorithm design and analysis,ergonomics,injury prevention,human factors,occupational safety,radar imaging,suicide prevention,system performance,indexing terms,radar,sensor fusion,data fusion,fusion,image analysis,statistics,vision | Radar,Object detection,Computer vision,Radar imaging,Algorithm design,Simulation,Sensor fusion,Artificial intelligence,Guard (information security),Engineering,Reactive system,Speedup | Journal |
Volume | Issue | ISSN |
8 | 1 | 1524-9050 |
Citations | PageRank | References |
72 | 5.52 | 1 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Giancarlo Alessandretti | 1 | 72 | 5.52 |
Alberto Broggi | 2 | 1527 | 178.28 |
Pietro Cerri | 3 | 240 | 16.96 |