The AVA Multi-View Dataset for Gait Recognition

  • David López-FernándezEmail author
  • Francisco José Madrid-Cuevas
  • Ángel Carmona-Poyato
  • Manuel Jesús Marín-Jiménez
  • Rafael Muñoz-Salinas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8703)


In this paper, we introduce a new multi-view dataset for gait recognition. The dataset was recorded in an indoor scenario, using six convergent cameras setup to produce multi-view videos, where each video depicts a walking human. Each sequence contains at least 3 complete gait cycles. The dataset contains videos of 20 walking persons with a large variety of body size, who walk along straight and curved paths. The multi-view videos have been processed to produce foreground silhouettes. To validate our dataset, we have extended some appearance-based 2D gait recognition methods to work with 3D data, obtaining very encouraging results. The dataset, as well as camera calibration information, is freely available for research purposes.


Human Gait Gait Recognition Camera Setup Gait Sequence Gait Energy Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work has been developed with the support of the Research Projects called TIN2012-32952 and BROCA both financed by Science and Technology Ministry of Spain and FEDER.


  1. 1.
    Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybern. 34, 334–352 (2004)CrossRefGoogle Scholar
  2. 2.
    Lee, C.P., Tan, A.W.C., Tan, S.C.: Gait recognition via optimally interpolated deformable contours. Pattern Recogn. Lett. 34, 663–669 (2013)CrossRefGoogle Scholar
  3. 3.
    Das Choudhury, S., Tjahjadi, T.: Gait recognition based on shape and motion analysis of silhouette contours. Comput. Vis. Image Underst. 117, 1770–1785 (2013)CrossRefGoogle Scholar
  4. 4.
    Zeng, W., Wang, C.: Human gait recognition via deterministic learning. Neural Netw. 35, 92–102 (2012)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Roy, A., Sural, S., Mukherjee, J.: Gait recognition using pose kinematics and pose energy image. Sig. Process. 92, 780–792 (2012)CrossRefGoogle Scholar
  6. 6.
    Huang, X., Boulgouris, N.: Gait recognition with shifted energy image and structural feature extraction. IEEE Trans. Image Process. 21, 2256–2268 (2012)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Bashir, K., Xiang, T., Gong, S.: Gait recognition without subject cooperation. Pattern Recogn. Lett. 31, 2052–2060 (2010)CrossRefGoogle Scholar
  8. 8.
    Barnich, O., Van Droogenbroeck, M.: Frontal-view gait recognition by intra- and inter-frame rectangle size distribution. Pattern Recogn. Lett. 30, 893–901 (2009)CrossRefGoogle Scholar
  9. 9.
    Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28, 316–322 (2006)CrossRefGoogle Scholar
  10. 10.
    Iwashita, Y., Ogawara, K., Kurazume, R.: Identification of people walking along curved trajectories. Pattern Recogn. Lett. 48(0), 60–69 (2014)CrossRefGoogle Scholar
  11. 11.
    Kusakunniran, W., Wu, Q., Zhang, J., Li, H.: Gait recognition under various viewing angles based on correlated motion regression. IEEE Trans. Circuits Syst. Video Technol. 22, 966–980 (2012)CrossRefGoogle Scholar
  12. 12.
    Krzeszowski, T., Kwolek, B., Michalczuk, A., Świtoński, A., Josiński, H.: View independent human gait recognition using markerless 3D human motion capture. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2012. LNCS, vol. 7594, pp. 491–500. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Lu, J., Tan, Y.P.: Uncorrelated discriminant simplex analysis for view-invariant gait signal computing. Pattern Recogn. Lett. 31, 382–393 (2010)CrossRefGoogle Scholar
  14. 14.
    Goffredo, M., Bouchrika, I., Carter, J., Nixon, M.: Self-calibrating view-invariant gait biometrics. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 40, 997–1008 (2010)CrossRefGoogle Scholar
  15. 15.
    Kusakunniran, W., Wu, Q., Li, H., Zhang, J.: Multiple views gait recognition using view transformation model based on optimized gait energy image. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1058–1064 (2009)Google Scholar
  16. 16.
    Bodor, R., Drenner, A., Fehr, D., Masoud, O., Papanikolopoulos, N.: View-independent human motion classification using image-based reconstruction. Image Vis. Comput. 27, 1194–1206 (2009)CrossRefGoogle Scholar
  17. 17.
    Makihara, Y., Mannami, H., Tsuji, A., Hossain, M., Sugiura, K., Mori, A., Yagi, Y.: The ou-isir gait database comprising the treadmill dataset. IPSJ Trans. Comput. Vis. Appl. 4, 53–62 (2012)Google Scholar
  18. 18.
    Shutler, J., Grant, M., Nixon, M.S., Carter, J.N.: On a large sequence-based human gait database. In: Proceedings of RASC, pp. 66–72. Springer (2002)Google Scholar
  19. 19.
    Gross, R., Shi, J.: The cmu motion of body (mobo) database. Technical report CMU-RI-TR-01-18, Robotics Institute, Pittsburgh, PA (2001)Google Scholar
  20. 20.
    Yu, S., Tan, D., Tan, T.: A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 4, pp. 441–444 (2006)Google Scholar
  21. 21.
    Nixon, M.S., Tan, T.N., Chellappa, R.: Human Identification Based on Gait, vol. 4. Springer, New York (2006)CrossRefGoogle Scholar
  22. 22.
    Sarkar, S., Phillips, P.J., Liu, Z., Vega, I.R., Grother, P., Bowyer, K.W.: The humanid gait challenge problem: data sets, performance, and analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27, 162–177 (2005)CrossRefGoogle Scholar
  23. 23.
    Wang, L., Tan, T., Ning, H., Hu, W.: Silhouette analysis-based gait recognition for human identification. IEEE Trans. Pattern Anal. Mach. Intell. 25, 1505–1518 (2003)CrossRefGoogle Scholar
  24. 24.
    Tan, D., Huang, K., Yu, S., Tan, T.: Efficient night gait recognition based on template matching. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 3, pp. 1000–1003 (2006)Google Scholar
  25. 25.
    Chalidabhongse, T., Kruger, V., Chellappa, R.: The umd database for human identification at a distance. Technical report, University of Maryland (2001)Google Scholar
  26. 26.
    Hofmann, M., Sural, S., Rigoll, G.: Gait recognition in the presence of occlusion: a new dataset and baseline algorithm. In: Proceedings of 19th International Conference on Computer Graphics, Visualization and Computer Vision (WSCG), Plzen, Czech Republic, 31 January 2011–03 February 2011Google Scholar
  27. 27.
    Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1395–1402 (2005)Google Scholar
  28. 28.
    Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local svm approach. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, pp. 32–36 (2004)Google Scholar
  29. 29.
    Vezzani, R., Cucchiara, R.: Video surveillance online repository (visor): an integrated framework. Multimedia Tools Appl. 50, 359–380 (2010)CrossRefGoogle Scholar
  30. 30.
    Tran, D., Sorokin, A.: Human activity recognition with metric learning. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 548–561. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  31. 31.
    Gkalelis, N., Kim, H., Hilton, A., Nikolaidis, N., Pitas, I.: The i3dpost multi-view and 3d human action/interaction database. In: Proceedings of the 2009 Conference for Visual Media Production, CVMP ’09, pp. 159–168. IEEE Computer Society, Washington, DC (2009)Google Scholar
  32. 32.
    Singh, S., Velastin, S., Ragheb, H.: Muhavi: a multicamera human action video dataset for the evaluation of action recognition methods. In: 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 48–55 (2010)Google Scholar
  33. 33.
    Weinland, D., Ronfard, R., Boyer, E.: Free viewpoint action recognition using motion history volumes. Comput. Vis. Image Underst. 104, 249–257 (2006)CrossRefGoogle Scholar
  34. 34.
    Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly, Cambridge (2008)Google Scholar
  35. 35.
    Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F., Marín-Jiménez, M.: Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recogn. 47, 2280–2292 (2014)CrossRefGoogle Scholar
  36. 36.
    Horprasert, T., Harwood, D., Davis, L.S.: A statistical approach for real-time robust background subtraction and shadow detection. In: Proceedings of IEEE ICCV, pp. 1–19 (1999)Google Scholar
  37. 37.
    Díaz-Más, L., Muñoz-Salinas, R., Madrid-Cuevas, F., Medina-Carnicer, R.: Shape from silhouette using dempster shafer theory. Pattern Recogn. 43, 2119–2131 (2010)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • David López-Fernández
    • 1
    Email author
  • Francisco José Madrid-Cuevas
    • 1
  • Ángel Carmona-Poyato
    • 1
  • Manuel Jesús Marín-Jiménez
    • 1
  • Rafael Muñoz-Salinas
    • 1
  1. 1.Computing and Numerical Analysis Department, Campus de Rabanales, Maimónides Institute for Biomedical Research (IMIBIC)University of CórdobaCórdobaSpain

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