Moving Quick Response Codes Identification: System Performance Analysis and Maximization by Optimization of Illuminance and Exposure Time

Muhammad S. Y. Sunarko, - Faridah, Agus Arif, Balza Achmad


We studied the relationship of Quick Response (QR) code identification system performances with illuminance on the QR code, exposure time of the camera, and relative moving speed. We studied the root causes of low identification performance problems on moving QR code as well. A physical experiment method study on a minimal working example of a real-time moving QR codes identification system with ZBar QR codes identification algorithm has done, and then the results were quantitatively and qualitatively analyzed. The values of illuminance on the QR code, exposure time of the camera, and relative moving speed used in the experiments were 140 lux - 640 lux, 0.7 ms - 22.2 ms, and 0 m/s - 2.5 m/s, respectively. Our data boundaries of the experiment results regarding exposure and motion blur (mediator variables) were respectively 0.108 lux·s - 12.476 lux·s and 0.0 pixel - 30.4 pixel. We identified physical phenomena in imaging, exposure and motion blur, could make the QR code image too dark/too bright and motion blurred. Such phenomena could make the QR code hard to identify and being the root cause of the problems. Our quantitative study proved that identification system performance is affected by illuminance, exposure time, and relative moving speed. Finally, we proposed a novel solution to overcome the problems by using a numerical method to compute the optimal illuminance on the QR code and the camera's exposure time for the given relative moving speed of the system.


Quick response code; identification performance; moving speed; exposure; illuminance; optimization.

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H. Zhang, C. Zhang, W. Yang, and C.-Y. Chen, “Localization and navigation using QR code for mobile robot in indoor environment,” in 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), Dec. 2015, pp. 2501–2506, doi: 10.1109/ROBIO.2015.7419715.

R. Taketani and H. Kobayashi, “A Proposal for Improving Estimation Accuracy of Localization Using QR codes and Image Sensors,” in IECON Proceedings (Industrial Electronics Conference), Oct. 2019, vol. 2019-Octob, pp. 6815–6820, doi: 10.1109/IECON.2019.8927589.

Z. Li and J. Huang, “Study on the use of Q-R codes as landmarks for indoor positioning: Preliminary results,” in 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS), Apr. 2018, pp. 1270–1276, doi: 10.1109/PLANS.2018.8373516.

P. Nazemzadeh, D. Fontanelli, D. Macii, and L. Palopoli, “Indoor Localization of Mobile Robots Through QR Code Detection and Dead Reckoning Data Fusion,” IEEE/ASME Trans. Mechatronics, vol. 22, no. 6, pp. 2588–2599, Dec. 2017, doi: 10.1109/TMECH.2017.2762598.

J. Tang, W. Zhu, and Y. Bi, “A Computer Vision-Based Navigation and Localization Method for Station-Moving Aircraft Transport Platform with Dual Cameras,” Sensors, vol. 20, no. 1, p. 279, Jan. 2020, doi: 10.3390/s20010279.

N. T. Truc and Y.-T. Kim, “Navigation Method of the Transportation Robot Using Fuzzy Line Tracking and QR Code Recognition,” Int. J. Humanoid Robot., vol. 14, no. 02, p. 1650027, Jun. 2017, doi: 10.1142/S0219843616500274.

A. S. Maner, D. Devasthale, V. Sonar, and R. Krishnamurti, “Mobile AR System using QR Code as Marker for EHV Substation Operation Management,” in 2018 20th National Power Systems Conference (NPSC), Dec. 2018, pp. 1–5, doi: 10.1109/NPSC.2018.8771834.

L. Cavanini et al., “A QR-code localization system for mobile robots: Application to smart wheelchairs,” in 2017 European Conference on Mobile Robots (ECMR), Sep. 2017, pp. 1–6, doi: 10.1109/ECMR.2017.8098667.

Y. Mashiba, H. E. B. Salih, N. Wakatsuki, K. Mizutani, and K. Zempo, “QR code without impairing the scenery and detection system for the visually impaired,” in 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020, Oct. 2020, pp. 888–892, doi: 10.1109/GCCE50665.2020.9291733.

X. W. Ye, T. H. Yi, C. Z. Dong, and T. Liu, “Vision-based structural displacement measurement: System performance evaluation and influence factor analysis,” Meas. J. Int. Meas. Confed., vol. 88, pp. 372–384, 2016, doi: 10.1016/j.measurement.2016.01.024.

J. Qian, X. Du, B. Zhang, B. Fan, and X. Yang, “Optimization of QR code readability in movement state using response surface methodology for implementing continuous chain traceability,” Comput. Electron. Agric., vol. 139, pp. 56–64, Jun. 2017, doi: 10.1016/j.compag.2017.05.009.

K. Liang et al., “Development and parameter optimization of automatic separation and identification equipment for grain tracing systems based on grain tracers with QR codes,” Comput. Electron. Agric., vol. 162, no. March, pp. 709–718, 2019, doi: 10.1016/j.compag.2019.04.039.

D. Mourtzis, V. Samothrakis, V. Zogopoulos, and E. Vlachou, “Warehouse Design and Operation using Augmented Reality technology: A Papermaking Industry Case Study,” Procedia CIRP, vol. 79, pp. 574–579, 2019, doi: 10.1016/j.procir.2019.02.097.

X. Yu et al., “Positioning, Navigation, and Book Accessing/Returning in an Autonomous Library Robot using Integrated Binocular Vision and QR Code Identification Systems,” Sensors, vol. 19, no. 4, p. 783, Feb. 2019, doi: 10.3390/s19040783.

H. Li, T. Chen, Y. Peng, and H. Li, “An intelligent vehicle-tracking system solution for indoor parking,” Appl. Geomatics, vol. 12, no. 4, pp. 481–490, Dec. 2020, doi: 10.1007/s12518-020-00321-8.

W. Hogpracha and S. Vongpradhip, “Recognition system for QR code on moving car,” in 10th International Conference on Computer Science and Education, ICCSE 2015, 2015, no. Iccse, pp. 14–18, doi: 10.1109/ICCSE.2015.7250210.

J. Brown, “ZBar bar code reader,” 2010.

I. Szentandrási, A. Herout, and M. Dubská, “Fast detection and recognition of QR codes in high-resolution images,” in Proceedings of the 28th Spring Conference on Computer Graphics - SCCG ’12, 2012, vol. 1, no. 212, pp. 129–136, doi: 10.1145/2448531.2448548.

Y. Liu, J. Yang, and M. Liu, “Recognition of QR Code with mobile phones,” in 2008 Chinese Control and Decision Conference, Jul. 2008, pp. 203–206, doi: 10.1109/CCDC.2008.4597299.

Y. He and Y. Yang, “An Improved Sauvola Approach on QR Code Image Binarization,” in 2019 IEEE 11th International Conference on Advanced Infocomm Technology, ICAIT 2019, Oct. 2019, pp. 6–10, doi: 10.1109/ICAIT.2019.8935907.

H. Pu, M. Fan, J. Yang, and J. Lian, “Quick response barcode deblurring via doubly convolutional neural network,” Multimed. Tools Appl., vol. 78, no. 1, pp. 897–912, Jan. 2019, doi: 10.1007/s11042-018-5802-2.

X. Yu and W. Xie, “Real-time recovery and recognition of motion blurry QR code image based on fractional order deblurring method,” IET Image Process., vol. 13, no. 6, pp. 923–930, May 2019, doi: 10.1049/iet-ipr.2018.5792.

S. Yu, “Learning from giving peer feedback on postgraduate theses: Voices from Master’s students in the Macau EFL context,” Assess. Writ., vol. 40, pp. 42–52, 2019, doi: 10.1016/j.asw.2019.03.004.

I. Jahr, “Lighting in Machine Vision,” in Handbook of Machine and Computer Vision, Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2017, pp. 63–178.

A. Godil, R. Bostelman, W. Shackleford, T. Hong, and M. Shneier, “Performance Metrics for Evaluating Object and Human Detection and Tracking Systems,” Gaithersburg, MD, Jul. 2014. doi: 10.6028/NIST.IR.7972.

H. Mattfeldt, “Camera Systems in Machine Vision,” in Handbook of Machine and Computer Vision, Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2017, pp. 317–397.

N. Holmes, “Camera Computer Interfaces,” in Handbook of Machine and Computer Vision, Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2017, pp. 431–503.

A. Rowlands, “Fundamental optical formulae,” in Physics of Digital Photography, IOP Publishing, 2017, pp. 1–62.

G. Patel and G. Panchal, “Quick response codes decodability improvements using error correction levels,” in 2017 International Conference on Intelligent Sustainable Systems (ICISS), Dec. 2017, no. Iciss, pp. 231–234, doi: 10.1109/ISS1.2017.8389404.

R. Petrella, M. Tursini, L. Peretti, and M. Zigliotto, “Speed measurement algorithms for low-resolution incremental encoder equipped drives: a comparative analysis,” in 2007 International Aegean Conference on Electrical Machines and Power Electronics, Sep. 2007, pp. 780–787, doi: 10.1109/ACEMP.2007.4510607.

J. J. Dziak, D. L. Coffman, S. T. Lanza, R. Li, and L. S. Jermiin, “Sensitivity and specificity of information criteria,” Brief. Bioinform., vol. 21, no. 2, pp. 553–565, Mar. 2020, doi: 10.1093/bib/bbz016.



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