Robust Video-Based Barcode Recognition via Online Sequential Filtering

  • Kim, Minyoung (Department of Electronics & IT Media Engineering, Seoul National University of Science & Technology)
  • Received : 2013.09.28
  • Accepted : 2014.03.11
  • Published : 2014.03.25


We consider the visual barcode recognition problem in a noisy video data setup. Unlike most existing single-frame recognizers that require considerable user effort to acquire clean, motionless and blur-free barcode signals, we eliminate such extra human efforts by proposing a robust video-based barcode recognition algorithm. We deal with a sequence of noisy blurred barcode image frames by posing it as an online filtering problem. In the proposed dynamic recognition model, at each frame we infer the blur level of the frame as well as the digit class label. In contrast to a frame-by-frame based approach with heuristic majority voting scheme, the class labels and frame-wise noise levels are propagated along the frame sequences in our model, and hence we exploit all cues from noisy frames that are potentially useful for predicting the barcode label in a probabilistically reasonable sense. We also suggest a visual barcode tracking approach that efficiently localizes barcode areas in video frames. The effectiveness of the proposed approaches is demonstrated empirically on both synthetic and real data setup.



  1. D. Chai and F. Hock, "Locating and decoding EAN-13 barcodes from images captured by digital cameras," in Fifth International Conference on Information, Communications and Signal Processing, Bangkok, Thailand, December 6-9, 2005, pp. 1595-1599.
  2. C. Zhang, J. Wang, S. Han, M. Yi, and Z. Zhang, "Automatic real-time barcode localization in complex scenes," in IEEE International Conference on Image Processing, Atlanta, GA, October 8-11, 2006, pp. 497-500.
  3. A. Tropf and D. Chai, "Locating 1-D bar codes in DCTdomain," ," in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Toulouse, France, May 14-19, 2006, pp. II-741-II-744.
  4. J. Yao, Y. F. Fan, and S. G. Pan, "Segmentation of bar code image with complex background based on template matching," Journal of Hehai University Changzhou, vol. 17, no. 4, pp. 24-27, Apr. 2003.
  5. X. S. Wu, L. Z. Qiao, and J. Deng, "A new method for bar code localization and recognition," in 2nd International Congress on Image and Signal Processing, Tianjin, China, October 17-19, 2009, pp. 1-6.
  6. A. R. Kim and S. Y. Rhee, "Recognition of natural hand gesture by using HMM," Journal of Korean Institute of Intelligent Systems, vol. 22, no. 5, pp. 639-645, Oct. 2012.
  7. K. E. Ko, S. M. Park, J. Y. Kim, and K. B. Sim, "HMMbased intent recognition system using 3D image reconstruction data," Journal of Korean Institute of Intelligent Systems, vol. 22, no. 2, pp. 135-140, Apr. 2012.
  8. K. A. Lee, D. J. Lee, J. H. Park, and M. G. Chun, "Face recognition using wavelet coefficients and hidden Markov model," Journal of Korean Institute of Intelligent Systems, vol. 13, no. 6, pp. 673-678, Dec. 2003.
  9. I. C. Kim, "Recognition of 3D hand gestures using partially tuned composite hidden Markov models," International Journal of Fuzzy Logic and Intelligent Systems, vol. 4, no. 2, pp. 236-240, Sep. 2004.
  10. M. G. Song, T. T. Pham, S. H. Min, J. Y. Kim, S. Y. Na, and S. T. Hwang, "Robust feature extraction based on image-based approach for visual speech recognition," Journal of Korean Institute of Intelligent Systems, vol. 20, no. 3, pp. 348-355, Jun. 2010.
  11. M. Black and A. Jepson, "EigenTracking: robust matching and tracking of articulated objects using a view-based representation," International Journal of Computer Vision, vol. 26, no. 1, pp. 63-84, Jan. 1998.
  12. M. Isard and A. Blake, "Contour tracking by stochastic propagation of conditional density," in Computer Vision ECCV '96, Lecture Notes in Computer Science Vol. 1064, B. Buxton and R. Cipolla, Eds. Heidelberg, Berlin: Springer, 1996, pp. 343-356.
  13. M. La Cascia, S. Sclaroff, and V. Athitsos, "Fast, reliable head tracking under varying illumination: an approach based on registration of texture-mapped 3D mod-els," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 4, pp. 322-336, Apr. 2000.
  14. A. D. Jepson, D. J. Fleet, and T. F. El-Maraghi, "Robust online appearance models for visual tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1296-1311, Oct. 2003.
  15. D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564-577, May 2003.
  16. G. D. Hager, M. Dewan, and C. V. Stewart, "Multiple kernel tracking with SSD," in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, June 27-July 2, 2004, pp. I-790-I-797.
  17. L. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proceedings of the IEEE, vol. 77, no. 2, pp. 257-286, Feb. 1989.