@Article{cmc.2020.012343,
AUTHOR = {Wei Sun, Xiaorui Zhang, Xiaozheng He, Yan Jin, Xu Zhang},
TITLE = {A Two-Stage Vehicle Type Recognition Method Combining the  Most Effective Gabor Features},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {3},
PAGES = {2489--2510},
URL = {http://www.techscience.com/cmc/v65n3/40183},
ISSN = {1546-2226},
ABSTRACT = {Vehicle type recognition (VTR) is an important research topic due to its 
significance in intelligent transportation systems. However, recognizing vehicle type on 
the real-world images is challenging due to the illumination change, partial occlusion 
under real traffic environment. These difficulties limit the performance of current stateof-art methods, which are typically based on single-stage classification without 
considering feature availability. To address such difficulties, this paper proposes a twostage vehicle type recognition method combining the most effective Gabor features. The 
first stage leverages edge features to classify vehicles by size into big or small via a 
similarity k-nearest neighbor classifier (SKNNC). Further the more specific vehicle type 
such as bus, truck, sedan or van is recognized by the second stage classification, which 
leverages the most effective Gabor features extracted by a set of Gabor wavelet kernels 
on the partitioned key patches via a kernel sparse representation-based classifier (KSRC). 
A verification and correction step based on minimum residual analysis is proposed to 
enhance the reliability of the VTR. To improve VTR efficiency, the most effective Gabor 
features are selected through gray relational analysis that leverages the correlation 
between Gabor feature image and the original image. Experimental results demonstrate 
that the proposed method not only improves the accuracy of VTR but also enhances the 
recognition robustness to illumination change and partial occlusion.},
DOI = {10.32604/cmc.2020.012343}
}