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A Logo-Based Approach for Recognising Multiple Products on a Shelf

  • Trisha MittalEmail author
  • B. Laasya
  • J. Dinesh Babu
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 16)

Abstract

This paper addresses detecting, localizing and recognizing various grocery products in retail store images. Our object recognition algorithm achieves this goal using just one image per product for training, assuming that the category of the products (like cereals, rice, etc.) is known. This algorithm uses logo detection as a precursor to product recognition. So, the first step involves detecting and classifying products, at a broader level, based on their brands. The second step is the finer classification step for recognizing and localizing the exact product label, which involves using colour information. This hierarchical approach limits the confusion in classifying similar looking products and outperforms product recognition that was implemented without logo detection. The algorithm was tested on 80 annotated grocery shelf images containing 238 different products that fall under 3 categories. This facilitates smarter inventory management in retail stores on a large scale and on a day to day basis for the visually impaired people.

Keywords

Object detection and recognition Grocery products Local feature Template matching 

References

  1. 1.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  2. 2.
    Swain, M.J., Ballard, D.H.: Indexing via color histograms. In: Active Perception and Robot Vision, pp. 261–273. Springer, Heidelberg (1992)Google Scholar
  3. 3.
    De la Torre, F., Black, M.J.: Robust principal component analysis for computer vision. In: Eighth IEEE International Conference on Computer Vision, 2001, ICCV 2001, Proceedings, vol. 1, pp. 362–369. IEEE (2001)Google Scholar
  4. 4.
    George, M., Floerkemeier, C.: Recognizing products: a per-exemplar-label image classification approach. In: Computer Vision–ECCV 2014, pp. 440–455. Springer (2014)Google Scholar
  5. 5.
    Guo, G., Jiang, T., Wang, Y., Gao, W.: Finding multiple object instances with occlusion. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 3878–3881. IEEE (2010)Google Scholar
  6. 6.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)CrossRefGoogle Scholar
  7. 7.
    Chui, H., Rangarajan, A.: A new point matching algorithm for non-rigid registration. Comput. Vis. Image Underst. 89(2), 114–141 (2003)CrossRefzbMATHGoogle Scholar
  8. 8.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Computer Vision–ECCV 2006, pp. 404–417. Springer, Heidelberg (2006)Google Scholar
  9. 9.
    Collet, A., Berenson, D., Srinivasa, S.S., Ferguson, D.: Object recognition and full pose registration from a single image for robotic manipulation. In: IEEE International Conference on Robotics and Automation, 2009, ICRA 2009, pp. 48–55. IEEE (2009)Google Scholar
  10. 10.
    Winlock, T.: ShelfScanner: toward real-time detection of groceries for the visually impaired (2010)Google Scholar
  11. 11.
    Skoczylas, M.: Detection of positions and recognition of brand logos visible on images captured using mobile devices. In: 2014 International Conference and Exposition on Electrical and Power Engineering (EPE), pp. 863–868. IEEE (2014)Google Scholar
  12. 12.
    Muja, M., Lowe, D.G.: Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2227–2240 (2014)CrossRefGoogle Scholar
  13. 13.
    Muja, M., Lowe, D.G.: Fast matching of binary features. In: 2012 Ninth Conference on Computer and Robot Vision (CRV), pp. 404–410. IEEE (2012)Google Scholar
  14. 14.
    Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: VISAPP, vol. 1(2) (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Information TechnologyIIIT BangaloreBangaloreIndia

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