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Sensors 2016, 16(11), 1947;

Context-Aware Fusion of RGB and Thermal Imagery for Traffic Monitoring

Visual Analysis of People Lab, Aalborg University, 9000 Aalborg, Denmark
Author to whom correspondence should be addressed.
Academic Editors: Xue-Bo Jin, Feng-Bao Yang, Shuli Sun and Hong Wei
Received: 29 July 2016 / Revised: 8 November 2016 / Accepted: 14 November 2016 / Published: 18 November 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
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In order to enable a robust 24-h monitoring of traffic under changing environmental conditions, it is beneficial to observe the traffic scene using several sensors, preferably from different modalities. To fully benefit from multi-modal sensor output, however, one must fuse the data. This paper introduces a new approach for fusing color RGB and thermal video streams by using not only the information from the videos themselves, but also the available contextual information of a scene. The contextual information is used to judge the quality of a particular modality and guides the fusion of two parallel segmentation pipelines of the RGB and thermal video streams. The potential of the proposed context-aware fusion is demonstrated by extensive tests of quantitative and qualitative characteristics on existing and novel video datasets and benchmarked against competing approaches to multi-modal fusion. View Full-Text
Keywords: context-aware fusion; traffic surveillance; segmentation context-aware fusion; traffic surveillance; segmentation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Alldieck, T.; Bahnsen, C.H.; Moeslund, T.B. Context-Aware Fusion of RGB and Thermal Imagery for Traffic Monitoring. Sensors 2016, 16, 1947.

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