A Two-Stage Vehicle Type Recognition Method Combining the Most Effective Gabor Features |
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Authors: | Wei Sun Xiaorui Zhang Xiaozheng He Yan Jin Xu Zhang |
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Affiliation: | 1.School of Automation, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
2 Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing
University of Information Science & Technology, Nanjing, 210044, China.
3 Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology,
Nanjing, 210044, China.
4 Rensselaer Polytechnic Institute, Troy, NY 12180, USA. |
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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. |
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Keywords: | Vehicle type recognition improved Canny algorithm Gabor filter k-nearest neighbor classification grey relational analysis kernel sparse representation two-stage classification |
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