A New Shape Feature for Vehicle Classification in Thermal Video Sequences

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Since thermal images represent only the temperature difference between objects and background and they have more blurred edges than color images, the segmented images from them have noisy object boundaries. Therefore, the well-known features developed for color images may not work well with thermal images. To overcome these limitations of thermal images, we propose a novel feature extraction method based on the target trait context (TTC). A robust keypoint detector is also proposed by analyzing the included angle moments of the object boundary points. At each keypoint, we define the boundary shape context and the normalized intensity context, which contain shape information and thermal distribution, respectively, of the object. These two contexts are combined to form a new feature set, the TTC. To validate our proposed feature extraction method, the keypoint repeatability test and the classification performance test were performed and compared with those from the previous methods. The experiment results show that the proposed feature works well for thermal video sequences and outperforms the previous methods in classification performance
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2016-07
Language
English
Article Type
Article
Keywords

OBJECT RECOGNITION; IMAGE FEATURES; RANDOM FORESTS; DESCRIPTORS; GRAPHS

Citation

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, v.26, no.7, pp.1363 - 1375

ISSN
1051-8215
DOI
10.1109/TCSVT.2015.2452780
URI
http://hdl.handle.net/10203/213903
Appears in Collection
EE-Journal Papers(저널논문)
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