Advertisement

Human Action Detection and Recognition Using SIFT and SVM

  • Praveen M. Dhulavvagol
  • Niranjan C. Kundur
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 801)

Abstract

Human action detection and recognition is the most trending research topic in applications like surveillance of videos, analysis of sports videos and many applications which involve human computer interaction. Many researchers are working on different algorithms to improve the accuracy of human detection. Identifying the actions of human from the given video is a challenging task. In the proposed paper combination of two different techniques is applied i.e. SVM and SIFT techniques are used to identify and recognize the human actions in a given video or image. To extract local features of the given video SIFT based technique is used. In this techniques initially we extract features based on the interest points at a particular point or frames, Mainly SIFT techniques involves 4 basic steps Scale-space extreme detection, Key-point localization, Orientation assignment and Key-point descriptor. Once the key features are extracted they are further classified using SVM classifier. In the results and discussion we perform the comparative analysis of these two techniques on a standard KTH dataset with running and hand clapping actions. The experimental results determine the overall accuracy of 82% for the actions: running and hand clapping actions.

Keywords

SIFT Scale-space SVM Action recognition Key-points 

References

  1. 1.
    Poppe, R.: A survey on vision-based human action recognition. J. Image Vis. Comput. 28(6), 976–990 (2009). Human Media Interaction Group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of TwenteCrossRefGoogle Scholar
  2. 2.
    Kellokumpu, V., Zhao, G., Pietikäinen, M.: Recognition of human actions using texture descriptors. Mach. Vis. Appl. 22(5), 767–780 (2009).  https://doi.org/10.1007/s00138-009-0233-8CrossRefGoogle Scholar
  3. 3.
    Bermejo Nievas, E., Deniz Suarez, O., Bueno García, G., Sukthankar, R.: Violence detection in video using computer vision techniques. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011. LNCS, vol. 6855, pp. 332–339. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-23678-5_39CrossRefGoogle Scholar
  4. 4.
    Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: ICPR Proceedings of the 17th International Conference, vol. 3 (2004).  https://doi.org/10.1109/icpr.2004.1334462
  5. 5.
    Ahmad, M., Lee, S.W.: Human action recognition using shape and CLG-motion flow from multi-view image sequences. Pattern Recogn. 41(7), 2237–2252 (2008)CrossRefzbMATHGoogle Scholar
  6. 6.
    Patil, R.A., Sahula, V., Mandal, A.S.: Facial expression recognition in image sequences using active shape model and SVM. In: Fifth UKSim European Symposium on Computer Modeling and Simulation (EMS), pp. 168–173 (2011)Google Scholar
  7. 7.
    Lai, K.-T., Hsieh, C.-H., Lai, M.-F., Chen, M.-S.: Human action recognition using key points displacement. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D., Meunier, J. (eds.) ICISP 2010. LNCS, vol. 6134, pp. 439–447. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-13681-8_51CrossRefGoogle Scholar
  8. 8.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)CrossRefGoogle Scholar
  9. 9.
    Hoang, L.U.T., Ke, S., Hwang, J., Tuan, P.V., Chau, T.N.: Quasi-periodic action recognition from monocular videos via 3D human models and cyclic HMMs. In: Proceedings of IEEE International Conference on Advanced Technologies for Communications (ATC), Hanoi, Vietnam, pp. 110–113 (2012)Google Scholar
  10. 10.
    Scheldt, 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 (2004)Google Scholar
  11. 11.
    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 (2011)Google Scholar
  12. 12.
    Hu, Y., Cao, L., Lv, F., Yan, S., Gong, Y., Huang, T.: Action detection in complex scenes with spatial and temporal ambiguities. In: Proceedings of the 12th IEEE International Conference on Computer Vision (ICCV), pp. 128–135 (2009)Google Scholar
  13. 13.
    Bregonzio, M., Xiang, T., Gong, S.: Fusing appearance and distribution information of interest points for action recognition. Pattern Recogn. 45(3), 1220–1234 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.KLE Technological UniversityHubballiIndia
  2. 2.JSS Academy of Technical EducationBangaloreIndia

Personalised recommendations