Thermal vision human classification and localization using bag of visual word

Sourabh Malpani, C. S. Asha, A. V. Narasimhadhan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


Human 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.

Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE Region 10 Conference, TENCON 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781509025961
Publication statusPublished - 08-02-2017
Event2016 IEEE Region 10 Conference, TENCON 2016 - Singapore, Singapore
Duration: 22-11-201625-11-2016

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference2016 IEEE Region 10 Conference, TENCON 2016

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering


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