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• W2555361934 startingPage "1947" @default.
• W2555361934 abstract "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." @default.
• W2555361934 created "2016-11-30" @default.
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• W2555361934 date "2016-11-18" @default.
• W2555361934 modified "2023-10-05" @default.
• W2555361934 title "Context-Aware Fusion of RGB and Thermal Imagery for Traffic Monitoring" @default.
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• W2555361934 doi "https://doi.org/10.3390/s16111947" @default.
• W2555361934 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5134606" @default.
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