title: Context-Aware Fusion of RGB and Thermal Imagery for Traffic Monitoring.
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.
authors.affiliation: Visual Analysis of People Lab, Aalborg University, 9000 Aalborg, Denmark.
authors.forename: Thiemo
authors.initials: T
authors.lastname: Alldieck
authors.affiliation: Visual Analysis of People Lab, Aalborg University, 9000 Aalborg, Denmark. cb@create.aau.dk.
authors.forename: Chris H
authors.initials: CH
authors.lastname: Bahnsen
authors.affiliation: Visual Analysis of People Lab, Aalborg University, 9000 Aalborg, Denmark. thiemoall@gmail.com.
authors.forename: Thomas B
authors.initials: TB
authors.lastname: Moeslund
keywords: context-aware fusion; segmentation; traffic surveillance