Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/6802
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dc.contributor.authorMalpani, S.
dc.contributor.authorAsha, C.S.
dc.contributor.authorNarasimhadhan, A.V.
dc.date.accessioned2020-03-30T09:46:09Z-
dc.date.available2020-03-30T09:46:09Z-
dc.date.issued2017
dc.identifier.citationIEEE Region 10 Annual International Conference, Proceedings/TENCON, 2017, Vol., , pp.3135-3139en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/6802-
dc.description.abstractHuman detection in thermal images has recently gained a lot of attention in computer vision due to its large number of applications. The characteristics of thermal images are poor illumination, low contrast due to capturing devices and poor environment conditions. Human classification and localization are being done using bag of visual word method. Bag of visual word method has been widely used for visible spectrum. In this work, we have extended it to thermal images. A new human detection scheme is present for thermal image using SURF features with Bag of Word. SURF has been compared with different binary feature descriptors. SURF feature descriptor outperforms BRISK and FREAK feature descriptors in terms of accuracy, F-score. � 2016 IEEE.en_US
dc.titleThermal vision human classification and localization using bag of visual worden_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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