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A Novel Fine-Grained Method for vehicle type recognition Based on the Locally Enhanced PCANet Neural Network

Journal of Computer Science & Technology 2018年第2期33卷 335-350页

作者： Qian Wang You-Dong Ding School of Computer Enqineering and Science Shan qhai University Shanqhai 200072 China Information Center Criminal Investigation Department of Shanghai Public Security Bureau Shanghai 200083 China Shanghai Engineering Research Center of Motion Picture Special Effects Shanghai 200072 China Shanghai Film Academy Shanghai University Shanghai 200072 China

In this paper, we propose a locally enhanced PCANet neural network for fine-grained classification of vehicles. The proposed method adopts the PCANet unsupervised network with a smaller number of layers and simple parameters compared with the majority of state-of-the-art machine learning methods. It simplifies calculation steps and manual labeling, and enables vehicle types to be recognized without time-consuming training. Experimental results show that compared with the traditional pattern recognition methods and the multi-layer CNN methods, the proposed method achieves optimal balance in terms of varying scales of sample libraries, angle deviations, and training speed. It also indicates that introducing appropriate local features that have different scales from the general feature is very instrumental in improving recognition rate. The 7-angle in 180° （12-angle in 360°） classification modeling scheme is proven to be an effective approach, which can solve the problem of suffering decrease in recognition rate due to angle deviations, and add the recognition accuracy in practice.

关键词： fine-grained classification PCANet local enhancement vehicle type recognition

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A Two-Stage vehicle type recognition Method Combining the Most Effective Gabor Features

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Computers, Materials & Continua 2020年第12期65卷 2489-2510页

作者： Wei Sun Xiaorui Zhang Xiaozheng He Yan Jin Xu Zhang School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science&TechnologyNanjing210044China Rensselaer Polytechnic Institute TroyNY 12180USA

vehicle type recognition(VTR)is an important research topic due to its significance in intelligent transportation ***,recognizing vehicle type on the real-world images is challenging due to the illumination change,partial occlusion under real traffic *** difficulties limit the performance of current state-of-art methods,which are typically based on single-stage classification without considering feature *** address such difficulties,this paper proposes a two-stage vehicle type recognition method combining the most effective Gabor *** 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 *** 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 *** 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.

关键词： 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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Real Time and Automatic vehicle type recognition System Design and Its Application

Real Time and Automatic Vehicle Type Recognition System Desi...

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2012 International Conference on Mechanical Engineering and Automation(ICMEA2012)

作者： Wei Zhan1, Junkai Yang2,a 1College of Computer Science Yangtze University Jingzhou, Hubei, China2International School Beijing University of Posts and Telecommunications Beijing, China

Via a fixed camera, real time video including moving vehicles of a highway toll station is be collected, with technology of digital image processing and recognition, all frames include vehicle image can be detected automatically from the video, and vehicle type will be recognized automatically too. The system includes four modules: reading video and decomposing it into frames;moving vehicle detection;vehicle image processing and vehicle type recognition from image. Tests show that the system design is simple and effective. vehicle image processing algorithm in the system is simpler than that of references and vehicle type recognition algorithm through counting black pixels number including in vehicle body contour is a new idea.

关键词： Image Processing Image recognition vehicle type recognition

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