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Front. Inform. Technol. Electron. Eng.  2013, Vol. 14 Issue (2): 98-106    DOI: 10.1631/jzus.C1200223
    
Dust collector localization in trouble of moving freight car detection system
Fu-qiang Zhou, Rong Zou, He Gao
School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing 100191, China
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Fu-qiang Zhou, Rong Zou, He Gao
School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing 100191, China
 全文: PDF 
摘要： For a long time, trouble detection and maintenance of freight cars have been completed manually by inspectors. To realize the transition from manual to computer-based detection and maintenance, we focus on dust collector localization under complex conditions in the trouble of moving freight car detection system. Using mid-level features which are also named flexible edge arrangement (FEA) features, we first build the edge-based 2D model of the dust collectors, and then match target objects by a weighted Hausdorff distance method. The difference is that the constructed weighting function is generated by the FEA features other than specified subjectively, which can truly reflect the most basic property regions of the 3D object. Experimental results indicate that the proposed algorithm has better robustness to variable lighting, different viewing angle, and complex texture, and it shows a stronger adaptive performance. The localization correct rate of the target object is over 90%, which completely meets the need of practical applications.
关键词： Hausdorff distance;  Weighting function;  Trouble detection;  Rail transportation    
Abstract: For a long time, trouble detection and maintenance of freight cars have been completed manually by inspectors. To realize the transition from manual to computer-based detection and maintenance, we focus on dust collector localization under complex conditions in the trouble of moving freight car detection system. Using mid-level features which are also named flexible edge arrangement (FEA) features, we first build the edge-based 2D model of the dust collectors, and then match target objects by a weighted Hausdorff distance method. The difference is that the constructed weighting function is generated by the FEA features other than specified subjectively, which can truly reflect the most basic property regions of the 3D object. Experimental results indicate that the proposed algorithm has better robustness to variable lighting, different viewing angle, and complex texture, and it shows a stronger adaptive performance. The localization correct rate of the target object is over 90%, which completely meets the need of practical applications.
Key words: Hausdorff distance    Weighting function    Trouble detection    Rail transportation
收稿日期: 2012-07-16 出版日期: 2013-01-31
CLC:  TP39  
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Fu-qiang Zhou
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Fu-qiang Zhou, Rong Zou, He Gao. c . Front. Inform. Technol. Electron. Eng., 2013, 14(2): 98-106.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1200223    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2013/V14/I2/98

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