Advertisement

Reconstruction of occluded ROI in multi-person gait based on numerical methods

  • Jasvinder Pal SinghEmail author
  • Sanjeev Jain
  • Sakshi Arora
  • Uday Pratap Singh
Regular Paper
  • 32 Downloads

Abstract

Occlusion is an important factor for analysis of human gait recognition in real-time scenarios. In multi-person gait (MPG) or dynamic occlusion, gait recognition is affected due to occluded body parts known as region of interests (ROIs). The aim of this article is to reconstruct the occluded ROIs and measure the errors associated with the reconstruction methods. The contribution of this article is threefold: firstly, we segment five dynamic ROIs; secondly, reconstruction of ROIs using Lagrange, piecewise cubic hermite (PCH) and cubic spline and thirdly, a comparison among the above methods in MPG scenario. We consider the human body into two parts, i.e., lower and upper body. In lower body, we have considered ankle, while knee in upper body: wrist, elbow, and shoulder have been considered. The dataset used in this study consists of dynamic occlusion scenarios. The quantitative assessment of the above methods are based on four parameters such as mean square error, root mean square error, mean absolute error and mean absolute percentage error. Results show that PCH consistently outperforms the other methods in the reconstruction of occluded ROIs in MPG scenario.

Keywords

Gait recognition Occlusion Interpolation Cubic spline Piecewise cubic hermite polynomial Lagrange 

Notes

References

  1. 1.
    Kale, A., Sundaresan, A., Rajagopalan, A.N.: Identification of humans using gait. IEEE Trans. Image Process. 13(9), 1163–1173 (2004).  https://doi.org/10.1109/tip.2004.832865 CrossRefGoogle Scholar
  2. 2.
    Lee, T.K.M., Belkhatir, M., Sanei, S.: A comprehensive review of past and present vision-based techniques for gait recognition. Multimed. Tools Appl. 72(3), 2833–2869 (2014).  https://doi.org/10.1007/s11042-013-1574-x CrossRefGoogle Scholar
  3. 3.
    Wang, Liang, Tan, Tieniu, Weiming, Hu, Ning, Huazhong: Automatic gait recognition based on statistical shape analysis. IEEE Trans. Image Process. 12(9), 1120–1131 (2003).  https://doi.org/10.1109/TIP.2003.815251 MathSciNetCrossRefGoogle Scholar
  4. 4.
    Nixon, M.S., Carter, J.N.: Automatic recognition by gait. Proc. IEEE (2006).  https://doi.org/10.1109/jproc.2006.886018 CrossRefzbMATHGoogle Scholar
  5. 5.
    Zeng, Wei, Wang, Cong, Li, Yuanqing: Model-based human gait recognition via deterministic learning. Cognit. Comput. 6(2), 218–229 (2014).  https://doi.org/10.1007/s12559-013-9221-4 CrossRefGoogle Scholar
  6. 6.
    Yoo, J.-H., Hwang, D., Moon, K.-Y., Nixon, M.S.: Automated human recognition by gait using neural network. In: First Workshops on Image Processing Theory, Tools and Applications (IPTA), pp. 1–6 (2008)Google Scholar
  7. 7.
    Yoo, Jang-Hee, Nixon, Mark S.: Automated markerless analysis of human gait motion for recognition and classification. ETRI J. 33(2), 259–266 (2011)CrossRefGoogle Scholar
  8. 8.
    Bouchrika, I.: Parametric elliptic Fourier descriptors for automated extraction of gait features for people identification. In: 12th International Symposium on Programming and Systems (ISPS), pp. 1–7 (2015)Google Scholar
  9. 9.
    Choudhury, S.D., Tjahjadi, T.: Clothing and carrying condition invariant gait recognition based on rotation forest. Pattern Recognit. Lett. 80, 1–7 (2016).  https://doi.org/10.1016/j.patrec.2016.05.009 CrossRefGoogle Scholar
  10. 10.
    Jia, S., Wang, L., Li, X.: View-invariant gait authentication based on silhouette contours analysis and view estimation. IEEE/CAA J. Autom. Sin. 2(2), 226–232 (2015).  https://doi.org/10.1109/jas.2015.7081662 MathSciNetCrossRefGoogle Scholar
  11. 11.
    Ji, Ning, Sanchez, Victor, Li, Chang-Tsun: On view-invariant gait recognition: a feature selection solution. IET Biom. 7(4), 287–295 (2018).  https://doi.org/10.1049/iet-bmt.2017.0151 CrossRefGoogle Scholar
  12. 12.
    Sharma, H., Grover, J.: Human identification based on gait recognition for multiple view angles. Int. J. Intell. Robot. Appl. (2018).  https://doi.org/10.1007/s41315-018-0061-y CrossRefGoogle Scholar
  13. 13.
    Li, Xiang, Makihara, Yasushi, Chi, Xu, Muramatsu, Daigo, Yagi, Yasushi, Ren, Mingwu: Gait energy response functions for gait recognition against various clothing and carrying status. Appl. Sci. 8(8), 1380 (2018).  https://doi.org/10.3390/app8081380 CrossRefGoogle Scholar
  14. 14.
    Yu, S., Chen, H., Wang, Q., Shen, L., Huang, Y.: Invariant feature extraction for gait recognition using only one uniform model. Neurocomputing 239, 81–93 (2017).  https://doi.org/10.1016/j.neucom.2017.02.006 CrossRefGoogle Scholar
  15. 15.
    Hofman, M., Sural, S., Rigoll, G.: Gait recognition in the presence of occlusion: a new dataset and baseline algorithms. In: Proceedings of the 19th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, pp. 99–104 (2011)Google Scholar
  16. 16.
    Roy, A., Sural, S., Mukherjee, J., Rigoll, G.: Occlusion detection and gait silhouette reconstruction from degraded scenes. Signal Image Video Process. 5(4), 415–430 (2011).  https://doi.org/10.1007/s11760-011-0245-5 CrossRefGoogle Scholar
  17. 17.
    Takemura, N., Makihara, Y., Muramatsu, D., Echigo, T., Yagi, Y.: Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ Trans. Comput. Vis. Appl. 10(4), 1–14 (2018)Google Scholar
  18. 18.
    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), pp. 441–444 (2006)Google Scholar
  19. 19.
    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(2), 162–177 (2005).  https://doi.org/10.1109/TPAMI.2005.39 CrossRefGoogle Scholar
  20. 20.
    Singh, J.P., Jain, S., Arora, S., Singh, U.P.: Vision-based gait recognition: a survey. IEEE Access 6, 70497–70527 (2018).  https://doi.org/10.1109/access.2018.2879896 CrossRefGoogle Scholar
  21. 21.
    Chen, X., Weng, J., Lu, W., Xu, J.: Multi-gait recognition based on attribute discovery. IEEE Trans. Pattern Anal. Mach. Intell. 40(7), 1697–1710 (2017).  https://doi.org/10.1109/tpami.2017.2726061 CrossRefGoogle Scholar
  22. 22.
    Tafazzoli, Faezeh, Safabakhsh, Reza: Model-based human gait recognition using leg and arm movements. Eng. Appl. Artif. Intell. 23(8), 1237–1246 (2010)CrossRefGoogle Scholar
  23. 23.
    Singh, J.P., Jain, S., Arora, S., Singh, U.P.: Dataset for human recognition under multi-gait scenario. Mendeley Data (2019).  https://doi.org/10.17632/py4zw6g7xc.2 CrossRefGoogle Scholar
  24. 24.
    Lishani, A.O., Boubchir, L., Khalifa, E., Bouridane, A.: Human gait recognition based on Haralick features. Signal Image Video Process. (2017).  https://doi.org/10.1007/s11760-017-1066-y CrossRefGoogle Scholar
  25. 25.
    Nandy, Anup, Chakraborty, Rupak, Chakraborty, Pavan: Cloth invariant gait recognition using pooled segmented statistical features. Neurocomputing 191, 117–140 (2016).  https://doi.org/10.1016/j.neucom.2016.01.002 CrossRefGoogle Scholar
  26. 26.
    Lopez-Fernandez, D., Madrid Cuevas, F.J., Carmona Poyato, A., Munoz Salinas, R., Medina Carnicer, R.: A new approach for multi-view gait recognition on unconstrained paths. J. Vis. Commun. Image Represent. 38, 396–406 (2016)CrossRefGoogle Scholar
  27. 27.
    Hofmann, M., Wolf, D., Rigoll, G.: Identification and reconstruction of complete gait cycles for person identification in crowded scenes. In: International Conference on Computer Vision Theory and Applications, pp. 594–597 (2011)Google Scholar
  28. 28.
    Chattopadhyay, Pratik, Sural, Shamik, Mukherjee, Jayanta: Frontal gait recognition from occluded scenes. Pattern Recogn. Lett. 63, 9–15 (2015).  https://doi.org/10.1016/j.patrec.2015.06.004 CrossRefGoogle Scholar
  29. 29.
    Isa, W.N.M., Alam, M.J., Eswaran, C.: Gait recognition using occluded data. In: IEEE Asia Pacific Conference on Circuits and Systems, pp. 344–347 (2010)Google Scholar
  30. 30.
    Chen, Xin, Yang, Tianqi, Xu, J.: Multi-gait identification based on multilinear analysis and multi-target tracking. Multimed. Tools Appl. 75(11), 6505–6532 (2016).  https://doi.org/10.1007/s11042-015-2585-6 CrossRefGoogle Scholar
  31. 31.
    Chen, Xin, Jiaming, Xu, Weng, Jian: Multi-gait recognition using hypergraph partition. Mach. Vis. Appl. 28(1–2), 117–127 (2017).  https://doi.org/10.1007/s00138-016-0810-6 CrossRefGoogle Scholar
  32. 32.
    Federolf, P.A.: A novel approach to solve the “missing marker problem” in marker-based motion analysis that exploits the segment coordination patterns in multi-limb motion data. PLoS ONE 8(10), 1–13 (2013).  https://doi.org/10.1371/journal.pone.0078689 CrossRefGoogle Scholar
  33. 33.
    Gloersen, O., Federolf, P.: Predicting missing marker trajectories in human motion data using marker interconnections. PLoS ONE 11(3), 1–14 (2016).  https://doi.org/10.1371/journal.pone.0152616 CrossRefGoogle Scholar
  34. 34.
    Liu, G., McMillan, L.: Estimation of missing markers in human motion capture. Vis. Comput. 22(9–11), 721–728 (2006)CrossRefGoogle Scholar
  35. 35.
    Aristidou, A., Cameron, J., Lasenby, J.: Real-time estimation of missing markers in human motion capture. In: 2nd International Conference on Bioinformatics and Biomedical Engineering, pp. 1343–1346 (2008).  https://doi.org/10.1109/icbbe.2008.665
  36. 36.
    Kharab, A., Guenther, R.B.: An Introduction to Numerical Methods A MATLAB Approach, 3rd edn, pp. 171–178. CRC Press, Boca Raton (2012)zbMATHGoogle Scholar
  37. 37.
    Howarth, S.J., Callaghan, J.P.: Quantitative assessment of the accuracy for three interpolation techniques in kinematic analysis of human movement. Comput. Methods Biomech. Biomed. Eng. 13(6), 847–855 (2010).  https://doi.org/10.1080/10255841003664701 CrossRefGoogle Scholar
  38. 38.
    Piecewise Cubic Hermite Interpolating Polynomial (PCHIP). [Online] http://www.ece.northwestern.edu/local-apps/matlabhelp/techdoc/ref/pchip.html. Accessed 1 Nov 2018
  39. 39.
    Tang, Siyu, Andriluka, Mykhaylo, Schiele, Bernt: Detection and tracking of occluded people. Int. J. Comput. Vis. 110(1), 58–69 (2014).  https://doi.org/10.1007/s11263-013-0664-6 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Jasvinder Pal Singh
    • 1
    Email author
  • Sanjeev Jain
    • 1
  • Sakshi Arora
    • 1
  • Uday Pratap Singh
    • 2
  1. 1.School of Computer Science and EngineeringShri Mata Vaishno Devi UniversityKatraIndia
  2. 2.School of MathematicsShri Mata Vaishno Devi UniversityKatraIndia

Personalised recommendations