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Recognizing human actions from silhouettes described with weighted distance metric and kinematics

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Abstract

A virtual particle random walking theory under variable velocities is presented in this paper. Under the proposed theory, the solutions of some two-dimensional Poisson equations, which are discretized by nine-point finite difference method and defined on the so-called spatial-temporal motion accumulative image stemming from human silhouettes, provide us the depth contour image for actions description. Although merely two-dimensional definition domain and concepts are related to the Poisson equations, both spatial and temporal evolution information of human actions are successfully included in the depth contour image owing to designating the travelling velocities of virtual particles according to the spatial-temporal motion accumulative image. In addition, it is worth noting that projecting three-dimensional human actions to the two-dimensional image descriptors contributes to much lower computation cost in the corresponding recognition algorithms, compared to those when using the three-dimensional spatial-temporal descriptors directly. In order to enhance the recognition accuracy, a hierarchical cascaded classifier is configured with cascading nearest neighbor classifiers, in each layer of which different kinds of shape and kinematic features of human actions are dealt with. Numerical experimental results on several public human action databases are illustrated to verify recognition performance improvements by means of the proposed algorithm.

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References

  1. Aggarwal JK, Ryoo MS (2011) Human activity analysis: A review. ACM Comput Surv (CSUR) 43(3):16

    Article  Google Scholar 

  2. Ahad MAR, Ogata T, Tan J, Kim H, Ishikawa S (2008) A complex motion recognition technique employing directional motion templates. Int J Innov Comput Inf Control 4(8):1943–1954

    Google Scholar 

  3. Ali S, Shah M (2010) Human action recognition in videos using kinematic features and multiple instance learning. IEEE Trans Pattern Anal Mach Intell 32(2):288–303

    Article  Google Scholar 

  4. Aubert G, Aujol JF (2014) Poisson skeleton revisited: a new mathematical perspective. J Math Imaging Vision 48(1):149–159

    Article  MathSciNet  MATH  Google Scholar 

  5. Bala A, Kumar S, Bhargava R, Agarwal M (2014) Surface reconstruction by evaluating height from gradient values 2014 IEEE international advance computing conference (IACC), IEEE, pp 1033–1036

    Chapter  Google Scholar 

  6. Bobick AF, Davis JW (2001) The recognition of human movement using temporal templates. IEEE Trans Pattern Anal Mach Intell 23(3):257–267

    Article  Google Scholar 

  7. Bregonzio M, Gong S, Xiang T (2009) Recognising action as clouds of space-time interest points 2009 IEEE conference on computer vision and pattern recognition (CVPR), IEEE, pp 1948–1955

    Chapter  Google Scholar 

  8. Byrne CL (1981) Applied Iterative Methods. Academic Press

  9. Chandrashekhar VH, Venkatesh K (2006) Action energy images for reliable human action recognition Proceedings of the Asian Symposium on Information Display (ASID 2006), pp 484–487

    Google Scholar 

  10. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection

  11. Elgammal A, Harwood D, Davis L (2000) Non-parametric model for background subtraction Computer VisionECCV 2000, Springer, pp 751–767

    Google Scholar 

  12. Gorelick L, Blank M, Shechtman E, Irani M, Basri R (2007) Actions as space-time shapes. IEEE Trans Pattern Anal Mach Intell 29(12):2247–2253

    Article  Google Scholar 

  13. Gorelick L, Galun M, Sharon E, Basri R, Brandt A (2006) Shape representation and classification using the poisson equation. IEEE Trans Pattern Anal Mach Intell 28(12):1991–2005

    Article  Google Scholar 

  14. Han J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28(2):316–322

    Article  Google Scholar 

  15. Jhuang H, Serre T, Wolf L, Poggio T (2007) A biologically inspired system for action recognition 2007 IEEE international conference on computer vision (ICCV), IEEE, pp 1–8

    Google Scholar 

  16. Jin G, Pappas TN, Neuhoff DL (2014) An adaptive lighting correction method for matched-texture coding 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 2006–2010

    Chapter  Google Scholar 

  17. Junejo IN, Dexter E, Laptev I, Perez P (2011) View-independent action recognition from temporal self-similarities. IEEE Trans Pattern Anal Mach Intell 33 (1):172–185

    Article  Google Scholar 

  18. Kantorov V, Laptev I (2014) Efficient feature extraction, encoding, and classification for action recognition Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp 2593–2600

    Chapter  Google Scholar 

  19. Kläser A, Marszalek M, Cordelia S (2008) A spatio-temporal descriptor based on 3d-gradients 2008 Proceedings of british machine vision conference

    Google Scholar 

  20. Kovashka A, Grauman K (2010) Learning a hierarchy of discriminative space-time neighborhood features for human action recognition 2008 IEEE computer society conference on computer vision and pattern recognition, IEEE, pp 2046–2053

    Google Scholar 

  21. Li N, Cheng X, Zhang S, Wu Z (2013) Recognizing human actions by bp-adaboost algorithm under a hierarchical recognition framework 2013 Proceedings of IEEE international conference on acoustics, speech and signal processing, IEEE, pp 3407–3411

    Chapter  Google Scholar 

  22. Ling Z, Zhao C, Pan Q, Wang Y, Yongmei C (2007) Analyzing human movements from silhouettes via fourier descriptor Proceedings of the IEEE international conference on automation and logistics, IEEE, pp 231–236

    Google Scholar 

  23. Montagna R, Finlayson GD (2013) Reducing integrability error of color tensor gradients for image fusion. IEEE Trans Image Process 22(10):4072–4085

    Article  MathSciNet  Google Scholar 

  24. Qian H, Mao Y, Xiang W, Wang Z (2010) Recognition of human activities using svm multi-class classifier. Pattern Recogn Lett 31(2):100–111

    Article  Google Scholar 

  25. Qian H, Zhou J, Mao Y, Yuan Y (2015) Learning and recognizing human actions from video via poisson equations Proceedings of the 34th Chinese Control Conference, pp 3680–3685

    Google Scholar 

  26. Rosser JB (1975) Nine-point difference solutions for poisson’s equation. Comput Math Appl 1(75):351–360

    Article  MATH  Google Scholar 

  27. Sadek S, Al-Hamadi A, Michaelis B, Sayed U (2012) An svm approach for activity recognition based on chord-length-function shape features International Conference on Pattern Recognition, (ICPR) 2012, IEEE, pp 765–768

    Google Scholar 

  28. Savarese S, Andrey D, Carlos Neibles J, Fei-Fei L (2008) Spatial-temporal correlatons for unsupervised action classification 2008 IEEE workshop on motion & video computing copper mountain, IEEE, pp 1–8

    Google Scholar 

  29. Schüldt C, Laptev I, Caputo B (2004) Recognizing human actions: A local svm approach International conference on pattern recognition, (ICPR) 2004, IEEE, pp 32–36

    Google Scholar 

  30. Sekma M, Mejdoub M, Amar CB (2015) Human action recognition based on multi-layer fisher vector encoding method. Pattern Recogn Lett 65(C):37–43

    Article  Google Scholar 

  31. Strikwerda JC (2004) Finite difference schemes and partial differential equations. Siam

  32. Veeraraghavan A, Chowdhury AR, Chellappa R (2004) Role of shape and kinematics in human movement analysis Computer Vision and Pattern Recognition, 2004 IEEE Computer Society Conference on, IEEE, pp I–730–I–737

    Google Scholar 

  33. Wang H, Kläser A, Schmid C, Liu CL (2013) Dense trajectories and motion boundary descriptors for action recognition. International Journal of Computer Vision 103

  34. Wang LM, Qiao Y, Tang X (2013) Motionlets: Mid-level 3d parts for human motion recognition IEEE Conference on Computer Vision and Pattern Recognition, pp 2674–2681

    Google Scholar 

  35. Weinland D, Ronfard R, Boyer E (2011) A survey of vision-based methods for action representation, segmentation and recognition. Comput Vis Image Underst 115 (2):224–241

    Article  Google Scholar 

  36. Whytock T, Belyaev A, Robertson NM (2014) Dynamic distance-based shape features for gait recognition. J Math Imaging Vision 50(3):314–326

    Article  MATH  Google Scholar 

  37. Yogarajah P, Condell JV, Prasad G (2011) P rw gei: Poisson random walk based gait recognition 2011 7th international symposium on image and signal processing and analysis (ISPA), IEEE, pp 662–667

    Google Scholar 

  38. Zhang Z, Tao D (2012) Slow feature analysis for human action recognition. IEEE Trans Pattern Anal Mach Intell 34(3):436–450

    Article  Google Scholar 

  39. Zivkovic Z (2004) Improved adaptive gaussian mixture model for background subtraction Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004. IEEE, vol 2, pp 28–31

Download references

Acknowledgments

The authors would like to thank the Nature Science Foundation of Jiangsu Province, China, under Grant No. BK20140860, and the National Natural Science Foundation (NNSF) of China under Grant No. 61573001

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Authors and Affiliations

  1. College of Energy and Electrical Engineering, Hohai University, No.8 Focheng West Road, Jiangning District, Nanjing, 211100, People’s Republic of China

    Huimin Qian, Jun Zhou & Yue Yuan

  2. School of Automation, Nanjing University of Science and Technology, No 200 Xiaolingwei, XuanWu District, Nanjing, People’s Republic of China

    Yaobin Mao

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  1. Huimin Qian
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  2. Jun Zhou
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  4. Yue Yuan
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Correspondence to Huimin Qian.

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Qian, H., Zhou, J., Mao, Y. et al. Recognizing human actions from silhouettes described with weighted distance metric and kinematics. Multimed Tools Appl 76, 21889–21910 (2017). https://doi.org/10.1007/s11042-017-4610-4

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  • DOI: https://doi.org/10.1007/s11042-017-4610-4

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