Multi-spectral pedestrian detection via image fusion and deep neural networks

French, Geoffrey, Finlayson, Graham and Mackiewicz, Michal ORCID: https://orcid.org/0000-0002-8777-8880 (2018) Multi-spectral pedestrian detection via image fusion and deep neural networks. Journal of Imaging Science and Technology, Color and Imaging Conference, 26th Color and Imaging Conference Final Program and Proceedings. pp. 176-181. ISSN 1062-3701

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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. © 2018 Society for Imaging Science and Technology.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Science > Research Groups > Colour and Imaging Lab
Depositing User:	LivePure Connector
Date Deposited:	20 Sep 2018 16:30
Last Modified:	20 Jun 2024 00:22
URI:	https://ueaeprints.uea.ac.uk/id/eprint/68324
DOI:	10.2352/J.lmagingSci.Technol.2018.62.5.050406

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