Vehicle classification based on images from visible light and thermal camerasopen access
- Authors
- Nam, Yunyoung; Nam, Yun-Cheol
- Issue Date
- 19-Jan-2018
- Publisher
- Hindawi Publishing Corporation
- Keywords
- Vehicle detection; Vehicle classification; Thermal camera; Entropy; Energy
- Citation
- Eurasip Journal on Image and Video Processing
- Journal Title
- Eurasip Journal on Image and Video Processing
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/6265
- DOI
- 10.1186/s13640-018-0245-2
- ISSN
- 1687-5176
1687-5281
- Abstract
- We propose novel vehicle detection and classification methods based on images from visible light and thermal cameras. These methods can be used in real-time smart surveillance systems. To classify vehicles by type, we extract the headlight and grill areas from the visible light and thermal images. We then extract texture characteristics from the images and use these as features for classifying different types of moving vehicles. We also extract several features from images obtained at night and during the day, which are the contrast, homogeneity, entropy, and energy. We validated our method experimentally and achieved that the accuracy of our visible image classifier was 92.7% and the accuracy of our thermal image classifier was 65.8% when vehicles were classified into six types such as SUV type, sedan type, RV type.
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- Appears in
Collections - College of Engineering > Department of Computer Science and Engineering > 1. Journal Articles
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