Advertisement

A Simple Human Activity Recognition Technique Using DCT

  • Aziz Khelalef
  • Fakhreddine AbabsaEmail author
  • Nabil Benoudjit
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10016)

Abstract

In this paper, we present a simple new human activity recognition method using discrete cosine transform (DCT). The scheme uses the DCT coefficients extracted from silhouettes as descriptors (features) and performs frame-by-frame recognition, which make it simple and suitable for real time applications. We carried out several tests using radial basis neural network (RBF) for classification, a comparative study against stat-of-the-art methods shows that our technique is faster, simple and gives higher accuracy performance comparing to discrete transform based techniques and other methods proposed in literature.

Keywords

Human activity recognition DCT transform Neural network Features extraction Classification 

References

  1. 1.
    Ke, S.-R., Thuc, L.H.L., Lee, Y.-J., Hwang, J.-N., Yoo, J.-H., Choi, K.-H.: A review on video-based human activity recognition. Computers 2(2), 88–131 (2013)CrossRefGoogle Scholar
  2. 2.
    Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: Proceedings of ICCV, pp. 1395–1402 (2005)Google Scholar
  3. 3.
    Dollár, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: Proceedings of the 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking And Surveillance, Beijing, China, pp. 65–72, 15–16 October 2005Google Scholar
  4. 4.
    Kumari, S., Mitra, S.K.: Human action recognition using DFT. In: Proceedings of the Third IEEE National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), Hubli, India, pp. 239–242, 15–17 December 2011Google Scholar
  5. 5.
    Ahmad, T., Rafique, J.: Using discrete cosine transform based features for human action recognition. J. Image Graph. 3(2) (2015)Google Scholar
  6. 6.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, vol. 2, pp. 1150–1157, 20–25 September 1999Google Scholar
  7. 7.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA, vol. 1, pp. 886–893, 20–26 June 2005Google Scholar
  9. 9.
    Lu, W., Little, J.J.: Simultaneous tracking and action recognition using the PCA-HOG descriptor. In: Proceedings of the 3rd Canadian Conference on Computer and Robot Vision, Quebec, PQ, Canada, p. 6, 7–9 June 2006Google Scholar
  10. 10.
    Ojala, T., Pjetikainen, M.: Multiresolution grey-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)CrossRefGoogle Scholar
  11. 11.
    Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. PAMI 29(6), 915–928 (2007)CrossRefGoogle Scholar
  12. 12.
    Lin, C., Hsu, F., Lin, W.: Recognizing human actions using NWFE-based histogram vectors. EURASIP J. Adv. Sig. Process. 2010, 9 (2010)Google Scholar
  13. 13.
    Nakazawa, A., Kato, H., Inokuchi, S.: Human tracking using distributed vision systems. In: Proceedings of IEEE Fourteenth International Conference on Pattern Recognition, Brisbane, QLD, Australia, vol. 1, pp. 593–596, 20 August 1998Google Scholar
  14. 14.
    Huo, F., Hendriks, E., Paclik, P., Oomes, A.H.J.: Markerless human motion capture and pose recognition. In: Proceedings of the 10th IEEE Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), London, UK, pp. 13–16, 6–8 May 2009Google Scholar
  15. 15.
    Sedai, S., Bennamoun, M., Huynh, D.: Context-based appearance descriptor for 3D human pose estimation from monocular images. In: Proceedings of IEEE Digital Image Computing: Techniques and Applications (DICTA), Melbourne, VIC, Australia, pp. 484–491, 1–3 December 2009Google Scholar
  16. 16.
    Abdrhman, A., Ukasha, M.: Contour compression of image watermarking using DCT transform & ramer method. In: 2nd International Conference on Mechanical, Electronics and Mechatronics Engineering (ICMEME 2013), London, UK, pp, 147–151, 17–18 June 2013Google Scholar
  17. 17.
    Zhao, R.M., Lian, H., Pang, H.W., Hu, B.N.: A watermarking algorithm by modifying AC coefficies in DCT domain. In: International Symposium on Information Science and Engineering, pp. 159–162. IEEE (2008)Google Scholar
  18. 18.
    Imtiaz, H., et al.: Human action recognition based on spectral domain features. In: 19th Annual Conference KES-2015, SingaporeGoogle Scholar
  19. 19.
    Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Weizmann database (2007). http://www.wisdom.weizmann.ac.il/~vision/SpaceTimeActions.html
  20. 20.
    Boiman, O., Irani, M.: Similarity by composition. In: Proceedings of Neural Information Processing Systems (NIPS)Google Scholar
  21. 21.
    Scovanner, P., Ali, S., Shah, M.: A 3-dimensional SIFT descriptor and its application to action recognition. In: Proceedings of ACM Multimedia, pp. 357–360 (2007)Google Scholar
  22. 22.
    Wang, L., Suter, D.: Recognizing human activities from silhouettes: motion subspace and factorial discriminative graphical model. In: Proceedings of CVPR, p. 8 (2007)Google Scholar
  23. 23.
    Kellokumpu, V., Zhao, G., Pietikäinen, M.: Texture based description of movements for activity analysis. In: Proceedings of VISAPP, vol. 1, pp. 206–213 (2008)Google Scholar
  24. 24.
    Kellokumpu, V., Zhao, G., Pietikäinen, M.: Human activity recognition using a dynamic texture based method. In: Proceedings of BMVC, p. 10 (2008)Google Scholar
  25. 25.
    Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th IEEE International Conference on Pattern Recognition (ICPR), Cambridge, UK, vol. 3, pp. 32–36, 23–26 August 2004Google Scholar
  26. 26.
    Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, AK, USA, pp. 1–8, 23–28 June 2008Google Scholar
  27. 27.
    Foroughi, H., Naseri, A., Saberi, A., Yazdi, H.S.: An eigenspace-based approach for human fall detection using integrated time motion image and neural network. In: Proceedings of IEEE 9th International Conference on Signal Processing, pp. 1499–1503 (2008)Google Scholar
  28. 28.
    Fiaz, M.K., Ijaz, B.: Vision based human activity tracking using artificial neural networks. In: Proceedings of IEEE International Conference on Intelligent and Advanced Systems (ICIAS), Kuala Lumpur, Malaysia, pp. 1–5, 15–17 June 2010Google Scholar
  29. 29.
    Duong, T.V., Bui, H.H., Phung, D.Q., Venkatesh, S.: Activity recognition and abnormality detection with the switching hidden semi-markov model. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA, vol. 1, pp. 838–845, 20–25 June 2005Google Scholar
  30. 30.
    Yamato, J., Ohya, J., Ishii, K.: Recognizing human action in time-sequential images using Hidden Markov Model. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Champaign, IL, USA, pp. 379–385, 15–18 June 1992Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Aziz Khelalef
    • 1
  • Fakhreddine Ababsa
    • 2
    Email author
  • Nabil Benoudjit
    • 1
  1. 1.Laboratoire d’Automatique Avancée et d’Analyse des Systèmes (LAAAS)Université Batna-2-FesdisAlgeria
  2. 2.Laboratoire Informatique, Biologie Integrative et Systèmes Complexes (IBISC)Université d’Evry Val d’EssonneÉvryFrance

Personalised recommendations