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Background Modelling, Analysis and Implementation for Thermographic Images
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design. (SMART)
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design. (SMART)ORCID iD: 0000-0001-5615-7347
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.ORCID iD: 0000-0003-1923-3843
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Background subtraction is one of the fundamental steps in the image-processing pipeline for distinguishing foreground from background. Most of the methods have been investigated with respect to visual images, in which case challenges are different compared to thermal images. Thermal sensors are invariant to light changes and have reduced privacy concerns. We propose the use of a low-pass IIR filter for background modelling in thermographic imagery due to its better performance compared to algorithms such as Mixture of Gaussians and K-nearest neighbour, while reducing memory requirements for implementation in embedded architectures. Based on the analysis of four different image datasets both indoor and outdoor, with and without people presence, the learning rate for the filter is set to 3×10-3 Hz and the proposed model is implemented on an Artix-7 FPGA.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Infrared; visual; pedestrian detection; smart camera; architecture; surveillance.
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:miun:diva-32445OAI: oai:DiVA.org:miun-32445DiVA: diva2:1165807
Conference
Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA 2017), Montreal, Canada; November 28 - December 1, 2017
Projects
City Movements
Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2017-12-19Bibliographically approved

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Shallari, IridaAnwar, QaiserImran, MuhammadO'Nils, Mattias

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