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Human Action Recognition Using Stereo Trajectories

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Pattern Recognition and Artificial Intelligence (MedPRAI 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1144))

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Abstract

This paper proposes a new method that uses a pair of uncalibrated stereo videos, without the need for three-dimensional reconstruction, for human action recognition (HAR). Two stereo views of the same scene, obtained from two different cameras, are used to create a set of two-dimensional trajectories. Then, we calculate disparities between them and fuse them with the trajectories, to obtain our disparity-augmented trajectories that is used in our HAR method. The obtained results have shown on average a 2.40% improvement, when using disparity-augmented trajectories, compared to using the classical 2D trajectory information only. Furthermore, we have also tested our method on the challenging Hollywood 3D dataset and, we have obtained competitive results, at a faster speed than some state of the art methods.

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References

  1. Johansson, G.: Visual perception of biological motion and a model for its analysis. Percept. Psychophysics 14(2), 201–211 (1973)

    Article  Google Scholar 

  2. Laptev, I., Lindeberg, T.: Interest point detection and scale selection in space-time. In: Griffin, L.D., Lillholm, M. (eds.) Scale-Space 2003. LNCS, vol. 2695, pp. 372–387. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-44935-3_26

    Chapter  MATH  Google Scholar 

  3. Laptev, I.: On space-time interest points. Int. J. Comput. Vis. 64(2–3), 107–123 (2005)

    Article  Google Scholar 

  4. Dollár, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: 2005 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 65–72. IEEE (2005)

    Google Scholar 

  5. Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)

    Google Scholar 

  6. Bregonzio, M., Gong, S., Xiang, T.: Recognising action as clouds of space-time interest points. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1948–1955. IEEE (2009)

    Google Scholar 

  7. Wang, H., Ullah, M.M., Klaser, A., Laptev, I., Schmid, C., et al.: Evaluation of local spatio-temporal features for action recognition. In: BMVC 2009-British Machine Vision Conference (2009)

    Google Scholar 

  8. Perš, J., Sulić, V., Kristan, M., Perše, M., Polanec, K., Kovačič, S.: Histograms of optical flow for efficient representation of body motion. Pattern Recogn. Lett. 31(11), 1369–1376 (2010)

    Article  Google Scholar 

  9. Li, L.-J., Su, H., Fei-Fei, L., Xing, E.P.: Object bank: a high-level image representation for scene classification & semantic feature sparsification. In: Advances in Neural Information Processing Systems, pp. 1378–1386 (2010)

    Google Scholar 

  10. Sadanand, S., Corso, J.J.: Action bank: a high-level representation of activity in video. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1234–1241. IEEE (2012)

    Google Scholar 

  11. Yao, A., Gall, J., Fanelli, G., Van Gool, L.J.: Does human action recognition benefit from pose estimation? In: BMVC, vol. 3, p. 6 (2011)

    Google Scholar 

  12. Jhuang, H., Gall, J., Zuffi, S., Schmid, C., Black, M.J.: Towards understanding action recognition. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 3192–3199. IEEE (2013)

    Google Scholar 

  13. Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R.: Berkeley MHAD: a comprehensive multimodal human action database. In: 2013 IEEE Workshop on Applications of Computer Vision (WACV), pp. 53–60. IEEE (2013)

    Google Scholar 

  14. Shotton, J., et al.: Real-time human pose recognition in parts from single depth images. Commun. ACM 56(1), 116–124 (2013)

    Article  Google Scholar 

  15. Barnachon, M., Bouakaz, S., Boufama, B., Guillou, E.: Ongoing human action recognition with motion capture. Pattern Recogn. 47(1), 238–247 (2014)

    Article  Google Scholar 

  16. Boufama, B., Habashi, P., Ahmad, I.S.: Trajectory-based human activity recognition from videos. In: 2017 3rd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). IEEE (2017)

    Google Scholar 

  17. Habashi, P., Boufama, B., Ahmad, I.S.: A better trajectory shape descriptor for human activity recognition. In: Karray, F., Campilho, A., Cheriet, F. (eds.) ICIAR 2017. LNCS, vol. 10317, pp. 330–337. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59876-5_37

    Chapter  Google Scholar 

  18. Wang, H., Kläser, A., Schmid, C., Liu, C.-L.: Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis. 103(1), 60–79 (2013)

    Article  MathSciNet  Google Scholar 

  19. Wang, H., Schmid, C.: Action recognition with improved trajectories. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 3551–3558. IEEE (2013)

    Google Scholar 

  20. Farnebäck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363–370. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45103-X_50

    Chapter  Google Scholar 

  21. Mademlis, I., Iosifidis, A., Tefas, A., Nikolaidis, N., Pitas, I.: Exploiting stereoscopic disparity for augmenting human activity recognition performance. Multimed. Tools Appl. 75(19), 11641–11660 (2016)

    Article  Google Scholar 

  22. Hadfield, S., Lebeda, K., Bowden, R.: Hollywood 3D: what are the best 3D features for action recognition? Int. J. Comput. Vis. 121(1), 95–110 (2017)

    Article  MathSciNet  Google Scholar 

  23. Matikainen, P., Hebert, M., Sukthankar, R.: Trajectons: action recognition through the motion analysis of tracked features. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 514–521. IEEE (2009)

    Google Scholar 

  24. Lucas, B.D., Kanade, T., et al.: An iterative image registration technique with an application to stereo vision (1981)

    Google Scholar 

  25. Messing, R., Pal, C., Kautz, H.: Activity recognition using the velocity histories of tracked keypoints. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 104–111. IEEE (2009)

    Google Scholar 

  26. Sun, J., Wu, X., Yan, S., Cheong, L.-F., Chua, T.-S., Li, J.: Hierarchical spatio-temporal context modeling for action recognition. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 2004–2011. IEEE (2009)

    Google Scholar 

  27. Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 6, 583–598 (1991)

    Article  Google Scholar 

  28. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  29. Varol, G., Salah, A.A.: Efficient large-scale action recognition in videos using extreme learning machines. Expert Syst. Appl. 42(21), 8274–8282 (2015)

    Article  Google Scholar 

  30. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm

    Article  Google Scholar 

  31. Wang, H., Klaser, A., Schmid, C., Liu, C.-L.: Action recognition by dense trajectories. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3169–3176. IEEE (2011)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science, University of Windsor, Windsor, Canada

    Pejman Habashi, Boubakeur Boufama & Imran Shafiq Ahmad

Authors
  1. Pejman Habashi
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  2. Boubakeur Boufama
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  3. Imran Shafiq Ahmad
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Editor information

Editors and Affiliations

  1. Larbi Tebessi University, Tebessa, Algeria

    Chawki Djeddi

  2. Istanbul Sabahattin Zaim University, Istanbul, Turkey

    Akhtar Jamil

  3. Bahria University, Islamabad, Pakistan

    Imran Siddiqi

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Habashi, P., Boufama, B., Ahmad, I.S. (2020). Human Action Recognition Using Stereo Trajectories. In: Djeddi, C., Jamil, A., Siddiqi, I. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2019. Communications in Computer and Information Science, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-030-37548-5_8

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  • DOI: https://doi.org/10.1007/978-3-030-37548-5_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37547-8

  • Online ISBN: 978-3-030-37548-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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