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An Automated Vision Based Change Detection Method for Planogram Compliance in Retail Stores

  • Muthugnanambika M.
  • Bagyammal T.
  • Latha Parameswaran
  • Karthikeyan VaiapuryEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 28)

Abstract

Planogram are visual representations of a store’s products and services designed to help retailers ensure that the right merchandise is consistently on display, and that inventory is controlled at a level that guarantees that the right number of products are on each and every shelf. The main objective of this work is to propose an algorithm using image processing and machine learning as its base to find and detect the changes in the arrangement of objects present in the retail stores. The proposed algorithm is capable of identifying void space, count objects of similar type and thus helps in tracking the changes. Blob detection superseded by classification using a discriminative machine learning approach with the extracted statistical features of the objects has been used in this proposed algorithm. Experimental results are quite promising and hence this algorithm can be used to detect any changes occurring in a scene.

Keywords

Blob detection Multiclass discriminative machine learning approach Change detection Planogram compliance 

Notes

Acknowledgements

We thank Amrita Vishwa Vidyapeetham for having provided the required resources in the Amrita-Cognizant Innovation Lab for carrying out the research work.

References

  1. 1.
    https://www.cognizant.com/InsightsWhitepapers/Planogram-Compliance-Making-It-Work.pdfGoogle Scholar
  2. 2.
    Moorthy, R., Behera, S., Verma, S., Bhargave, S., Ramanathan, P.: Applying image processing for detecting on-shelf availability and product positioning in retail stores. In: Proceedings of the Third International Symposium on Women in Computing and Informatics. ACM (2015)Google Scholar
  3. 3.
    Minu S., Shetty, A.: A comparative study of image change detection algorithms in MATLAB. Aquat. Procedia 4, 1366–1373 (2015)Google Scholar
  4. 4.
    Radke, R.J., Andra, S., Al-Kofahi, O., Roysam B.: Image change detection algorithms: A systematic survey. IEEE Trans. Image Process. 14(3), 294–307 (2005)Google Scholar
  5. 5.
    Al-doski, J., Mansor, S.B., Shafri, H.Z.M.: Support vector machine classification to detect land cover changes in Halabja City, Iraq. Business Engineering and Industrial Applications Colloquium (BEIAC), IEEE (2013)Google Scholar
  6. 6.
    Klaric, M.N., Claywell, B.C., Scott, G.J., Hudson, N.J., Sjahputera, O., Li, Y., Barratt, S.T., Keller, J.M., Davis, C.H.: GeoCDX: An automated change detection and exploitation system for high-resolution satellite imagery. IEEE Trans. Geosci. Remote Sens. 51(4), 2067–2086 (2013)Google Scholar
  7. 7.
    Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R., Voloshynovskiy, S.: From single image to list of objects based on edge and blob detection. In: International Conference on Artificial Intelligence and Soft Computing, Springer International Publishing (2014)Google Scholar
  8. 8.
    Kong, H., Akakin, H. C., Sarma, S. E.: A generalized laplacian of gaussian filter for blob detection and its applications. IEEE Trans. Cybern. 43(6):1719–1733 (2013)Google Scholar
  9. 9.
    Han, K. T. M., Uyyanonvara, B.: A survey of blob detection algorithms for biomedical images. Inform. Commun. In: 7th International Conference of IEEE, Technol. Embedded. Syst. (IC-ICTES) (2016)Google Scholar
  10. 10.
    Huang, M. L., Hung, Y. H., Lee, W. M., Li, R. K., Jiang, B. R.: SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier. Sci. World J. 2014:1–10 (2014)Google Scholar
  11. 11.
    Reddy, B. V., Reddy, A. S., Reddy, P. B.: BITSMSSC: Brain image tomography using SOM with multi SVM sigmoid classifier. Comput. Intell. Data Min. 2:497–505. Springer India (2016)Google Scholar
  12. 12.
    Biswas, S., Aggarwal, G., Chellappa, R.: An efficient and robust algorithm for shape indexing and retrieval. IEEE Trans. Multimedia 12(5):372–385 (2010)Google Scholar
  13. 13.
    Venkateswaran, K., Kasthuri, N., Jeni, D. D.: A survey on unsupervised change detection algorithms. In: International Conference on IEEE, Circuits, Power Comput. Technol. (ICCPCT) (2013)Google Scholar
  14. 14.
    Ramanathan, R., Soman, K.P., Rohini,, P.A., Dharshana, G.: Investigation and development of methods to solve multi-class classification problems. In: International Conference on IEEE, Adv. Recent Technol. Commun. Comput. (ARTCom'09) (2009)Google Scholar
  15. 15.
    Sampath, A., Sivaramakrishnan, A., Narayan, K., Aarthi, R.: A study of household object recognition using SIFT-based bag-of-words dictionary and SVMs. In: Proceedings of the International Conference on Soft Computing Systems, Springer India (2016)Google Scholar
  16. 16.
    Bagyammal, T., Parameswaran, L.: Context based image retrieval using image features. Int. J. Adv. Inform. Sci. Technol. 29 (2014)Google Scholar
  17. 17.
    Nene, S.A., Nayar, S.K., Murase, H.: Columbia object image library (COIL-100). Tech. Rep CUCS-006-96, February (1996)Google Scholar

Copyright information

© Springer International Publishing AG  2018

Authors and Affiliations

  • Muthugnanambika M.
    • 1
  • Bagyammal T.
    • 1
  • Latha Parameswaran
    • 1
  • Karthikeyan Vaiapury
    • 2
    Email author
  1. 1.Department of Computer Science and Engineering, Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia
  2. 2.TCS Innovation LabsChennaiIndia

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