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Action Recognition Framework Based on Normalized Local Binary Pattern

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Progress in Advanced Computing and Intelligent Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 713))

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Abstract

Human action recognition in computer vision has become dexterous in detecting the abnormal activities to fortify safe events. This paper presents an efficient action recognition algorithm which is based on local binary pattern (LBP). The implementation of this approach can be used for action recognition in small premises such as ATM rooms by focusing on the LBP feature extraction via spatiotemporal relations. We also focus on decreasing the descriptor values by normalizing computed histogram bins. The results through ATM dataset demonstrate the enhancement in action recognition problem under different extensions. The normalized features obtained are classified using random forest classifier. In our study, it is shown that normalized version of LBP surpasses the conventional LBP descriptor with an average accuracy of 83%.

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References

  1. Damaševičius, R., Vasiljevas, M., Šalkevičius, J., Woźniak, M.: Human activity recognition in AAL environments using random projections. Comput. Math. Methods Med. 2016, Article ID 4073584, 1–17 (2016)

    Article  MathSciNet  Google Scholar 

  2. Ahad, M., Tan, J., Kim, H., Ishikawa, S.: Motion history image: its variants and applications. Mach. Vis. Appl. 23(2), 255–281 (2010)

    Article  Google Scholar 

  3. Klaser, A., Marszalek, M., Schmid, C.: A spatiotemporal descriptor based on 3D-gradients. In: Proceedings of British Machine Vision Conference, pp. 995–1004 (2008)

    Google Scholar 

  4. Ji, S., Xu, W., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2013)

    Article  Google Scholar 

  5. Niebles, J.C., Li, F.-F: A hierarchical model of shape and appearance for human action classification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)

    Google Scholar 

  6. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of British Machine Vision Conference, pp. 384–396 (2002)

    Google Scholar 

  7. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study for texture measures with classification based on feature distributions. Pattern Recogn. 29(1), 51–59 (1996)

    Article  Google Scholar 

  8. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)

    Article  MathSciNet  Google Scholar 

  9. Guo, Z. et al. Accurate, pupil center location with the SIFT descriptor and SVM classifier. Int. J. Patt. Recogn. Artif. Intell. 30(4), 1–15 (2016)

    Article  Google Scholar 

  10. Chakraborty, B., Holte, M.B., Moeslund, T.B., Gonzàlez, J.: Selective spatio-temporal interest points. Comput. Vis. Image Underst. 116(3), 396–410 (2012)

    Article  Google Scholar 

  11. Bay, H., Tuytelaars, T., Gool, L.V.: Surf: speeded up robust features. In: Proceedings of the 9th European Conference on Computer Vision (ECCV), vol. 3951, pp. 404–417 (2006)

    Chapter  Google Scholar 

  12. Abedin, Md.Z., Dhar, P., Deb, K.: Traffic sign recognition using SURF. In: Speeded up Robust Feature Descriptor and Artificial Neural Network Classifier, pp. 198–201 (2016)

    Google Scholar 

  13. Hu, R., Collomosse, J.: A performance evaluation of gradient field hog descriptor for sketch based image retrieval. Comput. Vis. Image Understand. 117(7), 790–806 (2013)

    Article  Google Scholar 

  14. Chaudhry, R., Ravichandran, A., Hager, G., Vidal, R.: Histograms of oriented optical flow and Binet–Cauchy kernels on nonlinear dynamical systems for the recognition of human actions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1932–1939 (2009)

    Google Scholar 

  15. Mahbub, U., Imtiaz, H., Ahad, M.A.R.: An optical flow based approach for action recognition. In: Computer and Information Technology (ICCIT), Dhaka, Bangladesh, pp. 646–651 (2011)

    Google Scholar 

  16. Ojala, T., Pietikäinen, M., Mäenpää, T.T.: Multiresolution gray-scale and rotation invariant texture classification with local binary pattern. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  17. Zhang, Y.X., Zhao, Y.Q., Liu, Y., Jiang, L.Q., Chen, Z.W.: Identification of Wood Defects Based on LBP Features, pp. 4202–4205 (2016)

    Google Scholar 

  18. Maksymiv, O., Rak, T., Peleshko, D.: Video-Based Flame Detection using LBP-Based Descriptor: Influences of Classifiers Variety on Detection Efficiency, pp. 42–48 (2017)

    Article  Google Scholar 

  19. Pietikäinen, M., Ojala, T., Xu, Z.: Rotation-invariant texture classification using feature distributions. Pattern Recogn. 33(1), 43–52 (2000)

    Article  Google Scholar 

  20. Pietikäinen, M., Zhao, M.G.: Two decades of local binary patterns: a survey. In: Bingham, E., Kaski, S., Laaksonen, J., Lampinen, J., (eds.), Advances in Independent Component Analysis and Learning Machines, Elsevier, pp. 175–210 (2015)

    Google Scholar 

  21. Pietik¨ainen, M., Ojala, T., Nisula, J., Heikkinen, J.: Experiments with two industrial problems using texture classification based on feature distributions. Intelligent Robots and Computer Vision XIII: 3D Vision, Product Inspection, and Active Vision, vol. 2354, no. 1, pp. 197–204 (1994)

    Google Scholar 

  22. Zhao, G., Pietikainen, M.: Dynamic texture recognition using volume local binary patterns. In: ECCV, Workshop on Dynamical Vision, pp. 165–177 (2006)

    Google Scholar 

  23. Huang, Y., Wang, Y., Tan, T.: Combining statistics of geometrical and correlative features for 3D face recognition. In: Proceedings of the British Machine Vision Conference, pp. 879–888 (2006)

    Google Scholar 

  24. Fehr, J.: Rotational Invariant Uniform Local Binary Patterns for Full 3D Volume Texture Analysis. FINSIG (2007)

    Google Scholar 

  25. Sanserwal, V., Tripathi, V., Pandey, M., Chen, Z.: Comparative Analysis of Various Feature Descriptors for Efficient ATM Surveillance Framework, vol. 10, no. 13, pp. 181–187 (2017)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Graphic Era University, Dehradun, 248002, Uttarakhand, India

    Shivam Singhal & Vikas Tripathi

Authors
  1. Shivam Singhal
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  2. Vikas Tripathi
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Corresponding author

Correspondence to Shivam Singhal .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Rama Devi Women's University, Bhubaneswar, Odisha, India

    Bibudhendu Pati

  2. Department of Computer Science, Rama Devi Women's University, Bhubaneswar, Odisha, India

    Chhabi Rani Panigrahi

  3. Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India

    Sudip Misra

  4. Department of Computer Science, Central University of Rajasthan, Jaipur, Rajasthan, India

    Arun K. Pujari

  5. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, Rourkela, Odisha, India

    Sambit Bakshi

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Singhal, S., Tripathi, V. (2019). Action Recognition Framework Based on Normalized Local Binary Pattern. In: Pati, B., Panigrahi, C., Misra, S., Pujari, A., Bakshi, S. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 713. Springer, Singapore. https://doi.org/10.1007/978-981-13-1708-8_23

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  • DOI: https://doi.org/10.1007/978-981-13-1708-8_23

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1707-1

  • Online ISBN: 978-981-13-1708-8

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