@article{uea68324,
          volume = {Color and Imaging Conference, 26th Color and Imaging Conference Final Program and Proceedings},
           month = {November},
          author = {Geoffrey French and Graham Finlayson and Michal Mackiewicz},
           title = {Multi-spectral Pedestrian Detection via Image Fusion and Deep Neural Networks},
         journal = {Journal of Imaging Science and Technology},
             doi = {10.2352/J.lmagingSci.Technol.2018.62.5.050406},
           pages = {176--181},
            year = {2018},
             url = {https://ueaeprints.uea.ac.uk/id/eprint/68324/},
        abstract = {The use of multi-spectral imaging has been found to improve the accuracy of deep neural network-based pedestrian detection systems, particularly in challenging night time conditions in which pedestrians are more clearly visible in thermal long-wave infrared bands than in plain RGB. In this article, the authors use the Spectral Edge image fusion method to fuse visible RGB and IR imagery, prior to processing using a neural network-based pedestrian detection system. The use of image fusion permits the use of a standard RGB object detection network without requiring the architectural modifications that are required to handle multi-spectral input. We contrast the performance of networks trained using fused images to those that use plain RGB images and networks that use a multi-spectral input. {\copyright} 2018 Society for Imaging Science and Technology.}
}

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