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|Title:||Thermal vision human classification and localization using bag of visual word|
|Citation:||IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2017, Vol., , pp.3135-3139|
|Abstract:||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. � 2016 IEEE.|
|Appears in Collections:||2. Conference Papers|
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