Detector and Descriptor Based Recognition and Counting of Objects in Real Time Environment

  • Harsh Vikram Singh
  • Sarvesh Kumar Verma


The high resolution digital cameras made a notable impression on which manner conveyance system are developing during the last few years. But making a computer system acquit oneself similar to human vision system is always a challenging task. Computer Vision is an innovator to fulfill this challenging task. Viola-Jones is one of the object detection methods which provide competitive object detection in actual-time. In this paper we describe the features extraction and AdaBoost algorithm used in Viola-Jones detection method to build efficient cascade classifier for effective object detection.


Object detection Face detection Viola-Jones Haarlike features Integral image AdaBoost Classifiers cascade 


  1. 1.
    Rafael C. Gonzalez, Richard E. Woods, Handbook of “Digital Image Processing”, 2nd Edition published by Pearson Education (2002).Google Scholar
  2. 2.
    P. Viola, M. Jones, “Rapid object detection using a boosted cascade of simple features”, in: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 129–185 (2001).Google Scholar
  3. 3.
    H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, “Speeded-up robust features (SURF),” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, June 2008.CrossRefGoogle Scholar
  4. 4.
    Bassem Sheta, Mohamed Elhabiby, Nase El-Sheimy, “Assessments of different Speeded UP Robust Features SURF algorithm resolution for Pos estimation of UAV”, IJCSES, vol. 3, No. 5, pp. 21–22, October 2012.Google Scholar
  5. 5.
    K. Velmurugan, Santhosh Baboo, “Content-Based image retrieval using SURF and colour moment”, Global journal of computer science and technology (GJCST), vol. 11, pp. 125–147, (2011).Google Scholar
  6. 6.
    Peng-le, C., “Study over high-precision vision inspection based on RANSAC algorithm”, Journal of Convergence Information Technology, vol. 7, no. 20, pp. 33–38, (2012).Google Scholar
  7. 7.
    Kong Jun, Jitang Min, Alimujiang Yiming, “Research on Speeded UP Robust Features with RANSAC”, International journal of technical research and applications (IJTRA), vol. 5, pp. 18–24, (2013).Google Scholar
  8. 8.
    Bay, H., Tuytelaars, T., VanGool, L., “SURF: Speeded Up Robust Features”, In ECCV (1), pp. 404–417, (2006).Google Scholar
  9. 9.
    E. Rosten, R. Porter, and T. Drummond, “Faster and Better: A Machine Learning Approach to Corner Detection”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, pp. 105–119, (2010).CrossRefGoogle Scholar
  10. 10.
    E. Hsiao and M. Hebert, “Occlusion Reasoning for Object Detection under Arbitrary Viewpoint”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(9) pp. 1803–1815, (2014).CrossRefGoogle Scholar
  11. 11.
    Y. Tian, B. Fan, and F. Wu, “L2Net: Deep learning of discriminative patch descriptor in Euclidean space,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2017.Google Scholar
  12. 12.
    Z. Wang, B. Fan, and G. W. an Fuchao Wu, “Exploring local and overall ordinal information for robust feature description,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 11, pp. 2198–2211, 2016.CrossRefGoogle Scholar
  13. 13.
    Q. Gu, T. Takaki, and I. Ishii, “A fast multi-object extraction algorithm based on cell-based connected components labeling”, IEEE International Conference on Computer Vision (ICCV), vol. E95-D, no. 2, pp. 636–645, (2012).CrossRefGoogle Scholar
  14. 14.
    Baugh, G., Kokaram, A., “Feature-based object modeling for visual surveillance”, 15th IEEE International Conference on Image Processing, ICIP, pp. 1352–1355, (2008).Google Scholar
  15. 15.
    D. Hoiem, R. Sukthankar, H. Schneiderman, and L. Huston, “Object-Based Image Retrieval Using the Statistical Structure of Images”. Journal of Machine Learning Research, 02 pp. 490–497, (2004).Google Scholar
  16. 16.
    L. Liu, P. Fieguth, Y. Guo, X. Wang, and M. Pietikainen, “Local binary features for texture classification: Taxonomy and experimental study,” Pattern Recognition, vol. 62, pp. 135–160, 2017.CrossRefGoogle Scholar
  17. 17.
    Iscen, Ahmet, et al. “A comparison of dense region detectors for image search and fine-grained classification.” Image Processing, IEEE Transactions on (Volume: 24, Issue: 8), pp. 2369–2381, 2015.Google Scholar
  18. 18.
    L. Liu, P. Fieguth, G. Zhao, M. Pietikainen, and D. Hu, “Extended local binary patterns for face recognition,” Information Sciences, vol. 358–359, no. 1, pp. 56–72, 2016Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Harsh Vikram Singh
    • 1
  • Sarvesh Kumar Verma
    • 1
  1. 1.Department of ElectronicsKamla Nehru Institute of Technology (KNIT)SultanpurIndia

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