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

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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.
Original languageEnglish
Pages (from-to)176-181
Number of pages6
JournalJournal of Imaging Science and Technology
VolumeColor and Imaging Conference, 26th Color and Imaging Conference Final Program and Proceedings
Publication statusPublished - 12 Nov 2018

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