Human Action Recognition Using SURF and HOG Features from Video Sequences

Authors

  • Akila M  Department of Computer Applications, Bharathiar University, Coimbatore, Tamilnadu, India
  • Rajeswari R  Department of Computer Applications, Bharathiar University, Coimbatore, Tamilnadu, India

Keywords:

STIP, human action recognition, SURF, Bag-of-Features, HOG

Abstract

Human action recognition is important in a number of applications such as video indexing, video surveillance and human computer interaction. Hence, human action recognition has been greatly researched during the last decades; however, it is still regarded as a challenging task. In this paper, a human action recognition method is proposed which aims to improve action recognition using a combination of local and global features. For the local feature Speeded-Up Robust Features (SURF) are used and for global feature Histogram of Oriented Gradients (HOG) are used. Bag-of-Features representation of local features is used for representation of the features extraction and the actions are classified using Support Vector Machine. The proposed human action recognition system is tested with various action categories of videos from the KTH dataset. The experimental results show that the proposed method has better results in terms of accuracy.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-12-31

Issue

Volume 2, Issue 6, November-December-2017

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Akila M, Rajeswari R, " Human Action Recognition Using SURF and HOG Features from Video Sequences, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.1259-1264, November-December-2017.