应用网络摄像头的二维码动态识别技术
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摘要
伴随着二维码在现代生产、生活中的广泛应用,高效、准确地识别二维码变得愈发重要,但是传统技术仅限于识别特定位置的二维码,而且对图像画质有着较高要求。应用网络摄像头的二维码动态识别系统可以解决以上问题,系统包括四个模块;网络摄像头视频处理、图像增强、图像粗粒度定位与分割、二维码识别与信息获取。相比直接调用二维码识别算法获取二维码信息,拥有以上四个模块的系统漏检率更低,并且可以识别实时拍摄的视频中的二维码。
With the wide application of two-dimensional code in modern production and life, it has become increasingly important to identify two-dimensional code efficiently and accurately.However, traditional technology is limited to the identification of twodimensional code in specific locations,and it has high requirements on image quality.The dynamic identification system of two-dimensional code can solve the above problems, including four modules; network camera video processing, image enhancement,coarse-grained image localization and segmentation,and two-dimensional code recognition and information acquisition.Compared with direct use of two-dimensional code recognition algorithm to obtain two-dimensional code information,the system with the above four modules has a lower miss rate and can identify the two-dimensional code in real-time filmed video.
With the wide application of two-dimensional code in modern production and life, it has become increasingly important to identify two-dimensional code efficiently and accurately.However, traditional technology is limited to the identification of twodimensional code in specific locations,and it has high requirements on image quality.The dynamic identification system of two-dimensional code can solve the above problems, including four modules; network camera video processing, image enhancement,coarse-grained image localization and segmentation,and two-dimensional code recognition and information acquisition.Compared with direct use of two-dimensional code recognition algorithm to obtain two-dimensional code information,the system with the above four modules has a lower miss rate and can identify the two-dimensional code in real-time filmed video.
引文
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[2] Liao Z L, Huang T L, Wang R, et al. A method of image analysis for QR code recognition[C]//International Conference on Intelligent Computing and Integrated Systems. IEEE, 2010:250-253.
[3] Chen J, Wu B. A Otsu Threshold Segmentation Method Based on Rebuilding and Dimension Reduction of the Two-Dimensional Histogram[J]. Journal of Graphics, 2015.
[4] Kato Y, Deguchi D, Takahashi T, et al. Low Resolution QRCode Recognition by Applying Super-Resolution Using the Property of QR-Codes[C]//International Conference on Document Analysis and Recognition. IEEE Computer Society, 2011:992-996.
[5] Jtc1/Sc I. Information technology--Automatic identification and data capture techniques--QR Code 2005 bar code symbology specification[J]. 2006.
[6] Cheng X M, Hao Q, Zhang C, et al. Distortion Correction of a Quick Response Code Image[J]. Applied Mechanics&Materials, 2013, 431:312-317.
[7] Qian K, Yu Y, Wang D, et al. Design for two-dimensional barcode dynamic recognition system in the environment of largescale logistics[C]//Advanced Information Technology, Electronic and Automation Control Conference. IEEE, 2016:878-882.
[8] Tribak H, Moughyt S, Zaz Y, et al. Remote QR code recognition based on HOG and SVM classifiers[C]//International Conference on Informatics and Computing. IEEE, 2017:137-141.
[9]Qian K, Yu Y, Wang D, et al. Design for two-dimensional barcode dynamic recognition system in the environment of largescale logistics[C]//Advanced Information Technology, Electronic and Automation Control Conference. IEEE, 2016:878-882.
[10]Sun H, Ma Y, Wu H, et al. An improved OTSU's method for CT image boundary contour extraction[C]//IEEE International Conference on Imaging Systems and Techniques. IEEE, 2016:493-497.