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.
Originalsprog | Dansk |
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Tidsskrift | Sensors |
Vol/bind | 16 |
Udgave nummer | 11 |
ISSN | 1424-8220 |
DOI | |
Status | Udgivet - 18 nov. 2016 |
Emneord
- Context-aware fusion
- Traffic surveillance
- Segmentation