Skip to main content

Advertisement

Log in

Human activity recognition using mixture of heterogeneous features and sequential minimal optimization

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Automated detection and tracking of a person’s actions plays a vital role in surveillance systems. Human activity detection has been carried out by using a variety of features; including flow-based, spatio-temporal and interest points based. We have created a fusion of features by incorporating those which give better results. LBP, HOG, Haar wavelets, SIFT, velocity and displacement being the major ones. By employing the time efficiency and optimality of SMO to train SVM, we have trained our system for both single person and multi-human action classification with improved accuracy. A generalized hierarchy of actions has been presented in this paper to demonstrate the extension of our methodology. We have achieved an accuracy of 91.99% on combination of KTH and Weizmann dataset and 86.48% on multi-human dataset. We have introduced our self-generated multi-human activity dataset in the following paper.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Moussa MM et al (2015) An enhanced method for human action recognition. J Adv Res 6(2):163–169

    Article  Google Scholar 

  2. Kolekar MH, Dash DP (2016) Hidden markov model based human activity recognition using shape and optical flow based features. In: Region 10 conference (TENCON) (2016 IEEE) IEEE

  3. Yang J, Cheng J, Lu H, Human activity recognition based on the blob features. In: IEEE international conference on multimedia and expo, pp 358361, 2009

  4. Niebles JC, Fei-Fei L (2007) A hierarchical model of shape and appearance for human action classification. In: IEEE conference on computer vision and pattern recognition, 2007. CVPR’07. IEEE

  5. Ke S-R et al (2013) A review on video-based human activity recognition. Computers 2(2):88–131

    Article  MathSciNet  Google Scholar 

  6. Li W, Zhang Z, Liu Z (2010) Action recognition based on a bag of 3d points. In: 2010 IEEE computer society conference on computer vision and pattern recognition workshops (CVPRW). IEEE

  7. Li W, Zhang Z, Liu Z (2008) Expandable data-driven graphical modeling of human actions based on salient postures. IEEE Trans Circ Syst Video Technol 18(11):1499–1510

    Article  Google Scholar 

  8. Wang H et al (2013) Dense trajectories and motion boundary descriptors for action recognition. Int J Comput Vis 103(1):60–79

    Article  MathSciNet  Google Scholar 

  9. Kellokumpu V, Pietik ainen M, Heikkila J (2005) Human activity recognition using sequences of postures. MVA, pp 570–573

  10. Wang H, Schmid C (2013) Action recognition with improved trajectories. In: Proceedings of the IEEE international conference on computer vision

  11. Schuldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local SVM approach. In Proceedings of the 17th IEEE international conference on pattern recognition (ICPR), Cambridge, UK, 2326 August 2004; vol 3, pp 32–36

  12. Vapnik V (1999) The nature of statistical learning theory. Springer, New York

    MATH  Google Scholar 

  13. Hoang LUT, Ke S, Hwang J, Tuan PV, Chau TN (2012) 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, 1012 October 2012; pp 110–113

  14. Yamato J, Ohya J, Ishii K ( 1992) Recognizing human action intime- sequential images using hidden markov model. In: IEEE computer society conference on computer vision and pattern recognition, pp 379–385

  15. Brand M, Oliver N, Pentland A (1997) Coupled hidden markov models for complex action recognition. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR), San Juan, PR, USA, 1719 June 1997; pp 994–999

  16. Duong TV, Bui HH, Phung DQ, Venkatesh S (2005) 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 (2005) June 2005; vol 1, pp 838–845

  17. Fiaz MK, Ijaz B (2010) Vision based human activity tracking using artificial neural networks. In: Proceedings of IEEE international conference on intelligent and advanced systems (ICIAS), Kuala Lumpur, Malaysia, 1517 June 2010; pp 15

  18. Umakanthan S, Denman S, Fookes C, Sridharan S (2014) Activity recognition using binary tree SVM. In: IEEE workshop on statistical signal processing, pp 248–251

  19. Platt J (1998) Sequential minimal optimization: a fast algorithm for training support vector machines. Technical Report MSR-TR-98-14, Microsoft Research

  20. Kolekar MH, Sengupta S (2004) Hidden markov model based structuring of cricket video sequences using motion and color features. In: Indian conference on computer vision graphics and image processing, pp 632–637

  21. Thurau C, Hlavc V (2008) Pose primitive based human action recognition in videos or still images. In: IEEE conference on computer vision and pattern recognition, 2008. CVPR 2008. IEEE

  22. Chen M, Hauptmann A (2009) MoSIFT: recognizing human actions in surveillance videos. In: CMU-CS-09-161, Carnegie Mellon University

  23. Chathuramali KG, Manosha, Rodrigo R (2012) Faster human activity recognition with SVM. In: 2012 international conference on advances in ICT for emerging regions (ICTer). IEEE

  24. Chathuramali KM, Rodrigo R (2012) Faster human activity recognition with SVM. In: International conference on advances in ICT for emerging regions, pp 197–203

  25. Liu L, Wang S, Su G, Huang ZG, Liu M (2017) Towards complex activity recognition using a Bayesian network-based probabilistic generative framework. Pattern Recogn 68:295–309

    Article  Google Scholar 

  26. Brendel W, Fern A, Todorovic S. Probabilistic event logic for interval-based event recognition. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR) 2011 Jun 20, pp 3329–3336. IEEE

  27. Abdul-Azim HA, Hemayed EE (2015) Human action recognition using trajectory-based representation. Egypt Inform J 16:187198

    Google Scholar 

  28. Chaquet JM, Enrique J, Carmona, Fernndez-Caballero A (2013) A survey of video datasets for human action and activity recognition. Comput Vis Image Underst 117(6):633–659

    Article  Google Scholar 

  29. Gilbert A, Illingworth J, Bowden R (2008) Scale invariant action recognition using compound features mined from dense spatio-temporal corners. In: European conference on computer vision. Springer, Berlin

  30. Uemura H, Ishikawa S, Mikolajczyk K. Feature tracking and motion compensation for action recognition. In: Proc. of BMVA british machine vision conference

  31. Schuldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local SVM approach. In: proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004, vol 3. IEEE

  32. Gilbert A, Illingworth J, Bowden R (2009) Fast realistic multi-action recognition using mined dense spatio-temporal features. In: 2009 IEEE 12th international conference on computer vision. IEEE

  33. Gorelick L et al (2007) Actions as space-time shapes. IEEE Trans Pattern Anal Mach Intell 29(12):2247–2253

    Article  Google Scholar 

  34. Uddin Md, Zia et al (2013) An indoor human activity recognition system for smart home using local binary pattern features with hidden markov models. Indoor Built Environ 22(1):289–298

    Article  Google Scholar 

  35. Mattivi R, Shao L (2009) Human action recognition using LBP-TOP as sparse spatio-temporal feature descriptor. In: International conference on computer analysis of images and patterns. Springer, Berlin

  36. Kellokumpu V, Zhao G, Pietikinen M (2011) Recognition of human actions using texture descriptors. Mach Vis Appl 22(5):767–780

    Article  Google Scholar 

  37. Bruzzone L, Persello C (2009) A novel approach to the selection of spatially invariant features for the classification of hyperspectral images with improved generalization capability. IEEE Trans Geosci Remote Sens 47(9):3180–3191

    Article  Google Scholar 

  38. Joachims T. Training linear SVMs in linear time. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 2006

  39. Blank M et al (2005) Actions as space-time shapes. In: Tenth IEEE international conference on computer vision, ICCV 2005, vol 2. IEEE, 2005

  40. Schindler K, Van Gool L (2008) Action snippets: how many frames does human action recognition require?. In: IEEE conference on computer vision and pattern recognition, 2008. CVPR 2008. IEEE

  41. Jhuang H et al (2007) A biologically inspired system for action recognition. In: IEEE 11th international conference on computer vision, ICCV 2007. IEEE, 2007

  42. Fathi A, Mori G (2008) Action recognition by learning mid-level motion features. In: IEEE conference on computer vision and pattern recognition, 2008. CVPR 2008. IEEE

  43. Klaser A, Marszaek M, Schmid C (2008) A spatio-temporal descriptor based on 3D-gradients. In: BMVC

  44. Laptev I, Marszalek M, Schmid C, Rozenfeld B (2008) Learning realistic human actions from movies. In: IEEE CVPR

  45. Abdul-Azim HA, Hemayed EE (2015) Human action recognitionusing trajectory-based representation. Egypt Inform J 16:187198

    Google Scholar 

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

    Article  Google Scholar 

  47. Raja K, Laptev I, Perez P, Oisel L (2011) Joint pose estimation and action recognition in image graphs. In: International conference on image processing, Brussels, Belgium, Sept. 2011, pp 2528

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Humza Naveed or Gulraiz Khan.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Naveed, H., Khan, G., Khan, A.U. et al. Human activity recognition using mixture of heterogeneous features and sequential minimal optimization. Int. J. Mach. Learn. & Cyber. 10, 2329–2340 (2019). https://doi.org/10.1007/s13042-018-0870-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-018-0870-1

Keywords