Matches in UGent Biblio for { <https://biblio.ugent.be/publication/1993468#aggregation> ?p ?o. }
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- aggregation classification "C1".
- aggregation creator B114545.
- aggregation creator B114546.
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- aggregation date "2011".
- aggregation format "application/pdf".
- aggregation hasFormat 1993468.bibtex.
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- aggregation isPartOf urn:isbn:9781457720062.
- aggregation language "eng".
- aggregation publisher "IEEE".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Technology and Engineering".
- aggregation title "Non-overlapping multi-camera detection and tracking of vehicles in tunnel surveillance".
- aggregation abstract "We propose a real-time multi-camera tracking approach to follow vehicles in a tunnel surveillance environment with multiple non-overlapping cameras. In such system, vehicles have to be tracked in each camera and passed correctly from one camera to another through the tunnel. This task becomes extremely difficult when intra-camera errors are accumulated. Most typical issues to solve in tunnel scenes are due to low image quality, poor illumination and lighting from the vehicles. Vehicle detection is performed using Adaboost detector, speeded up by separating different cascades for cars and trucks improving general accuracy of detection. A Kalman Filter with two observations, given by the vehicle detector and an averaged optical flow vector, is used for single-camera tracking. Information from collected tracks is used for feeding the inter-camera matching algorithm, which measures the correlation of Radon transform-like projections between the vehicle images. Our main contribution is a novel method to reduce the false positive rate induced by the detection stage. We impose recall over precision in the detection correctness, and identify false positives patterns which are then included subsequently in a high-level decision making step. Results are presented for the case of 3 cameras placed consecutively in an inter-city tunnel. We demonstrate the increased tracking performance of our method compared to existing Bayesian filtering techniques for vehicle tracking in tunnel surveillance.".
- aggregation authorList BK293877.
- aggregation endPage "596".
- aggregation startPage "591".
- aggregation aggregates 3052613.
- aggregation isDescribedBy 1993468.
- aggregation similarTo DICTA.2011.105.
- aggregation similarTo LU-1993468.