Abstract
Gait recognition has received significant attention in the recent years due to its applications in numerous fields of computer vision, particularly in automated person identification in visual surveillance and monitoring systems. In this paper, we propose a novel algorithm for gait recognition using spatio-temporal motion characteristics of a person. The proposed algorithm consists of four steps. First, motion features are extracted from video sequence which are used to generate a codebook in the second step. In a third step, the local descriptors are encoded using Fisher vector encoding. Finally, the encoded features are classified using linear Support Vector Machine (SVM). The performance of the proposed algorithm is evaluated and compared with state-of-the-art on two widely used gait databases TUM GAID and CASIA-A. The recognition results demonstrate the effectiveness of the proposed algorithm.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bashir, K., Xiang, T., Gong, S.: Gait recognition without subject cooperation. Pattern Recognit. Lett. 31(13), 2052–2060 (2010)
Bashir, K., Xiang, T., Gong, S., Mary, Q.: Gait representation using flow fields. In: BMVC, pp. 1–11 (2009)
Bay, H., Tuytelaars, T., Van Gool, L.: Surf: speeded up robust features. In: ECCV 2006, pp. 404–417 (2006)
Bouchrika, I., Nixon, M.S.: Model-based feature extraction for gait analysis and recognition. In: Gagalowicz, A., Philips, W. (eds.) MIRAGE 2007. LNCS, vol. 4418, pp. 150–160. Springer, Heidelberg (2007). doi:10.1007/978-3-540-71457-6_14
Chai, Y., Wang, Q., Jia, J., Zhao, R.: A novel human gait recognition method by segmenting and extracting the region variance feature. IEEE ICPR 4, 425–428 (2006)
Chen, C., Liang, J., Zhao, H., Hu, H., Tian, J.: Frame difference energy image for gait recognition with incomplete silhouettes. Pattern Recognit. Lett. 30(11), 977–984 (2009)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE CVPR, vol. 1, pp. 886–893, June 2005
Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: ECCV, pp. 428–441 (2006)
Fan, R.E., et al.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)
Gianaria, E., Balossino, N., Grangetto, M., Lucenteforte, M.: Gait characterization using dynamic skeleton acquisition. In: Proceedings of International Workshop Multimedia Signal Process, (MMSP), pp. 440–445, September 2013
Hofmann, M., Bachmann, S., Rigoll, G.: 2.5D gait biometrics using the depth gradient histogram energy image. In: IEEE BTAS, pp. 399–403 (2012)
Khan, M.H., Helsper, J., Yang, C., Grzegorzek, M.: An automatic vision-based monitoring system for accurate Vojta-therapy. In: IEEE/ACIS ICIS, pp. 1–6 (2016)
Khan, M.H., Helsper, J., Boukhers, Z., Grzegorzek, M.: Automatic recognition of movement patterns in the Vojta-therapy using RGB-D data. In: IEEE ICIP, pp. 1235–1239 (2016)
Khan, M.H., Shirahama, K., Farid, M.S., Grzegorzek, M.: Multiple human detection in depth images. In: IEEE International Workshop on MMSP, pp. 1–6 (2016)
Kusakunniran, W.: Attribute-based learning for gait recognition using spatio-temporal interest points. Image Vis. Comput. 32(12), 1117–1126 (2014)
Lam, T.H., Cheung, K.H., Liu, J.N.: Gait flow image: a silhouette-based gait representation for human identification. Pattern Recognit. 44(4), 973–987 (2011)
Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: IEEE CVPR, pp. 1–8, June 2008
Little, J., Boyd, J.: Recognizing people by their gait: the shape of motion. Videre J. Comput. Vis. Res. 1(2), 1–32 (1998)
Lowe, D.G.: Object recognition from local scale-invariant features. In: EEE ICCV, vol. 2, pp. 1150–1157 (1999)
Man, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 316–322 (2006)
McLachlan, G., Peel, D.: Finite Mixture Models. Wiley, New York (2004)
Nixon, M.S., Tan, T., Chellappa, R.: Human Identification Based on Gait. Springer, Heidelberg (2010)
Peng, X., Wang, L., Wang, X., Qiao, Y.: Bag of visual words and fusion methods for action recognition: comprehensive study and good practice. Comput. Vis. Image Underst. 150, 109–125 (2016). http://www.sciencedirect.com/science/article/pii/S1077314216300091
Peng, X., Zou, C., Qiao, Y., Peng, Q.: Action recognition with stacked fisher vectors. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, pp. 581–595. Springer, Heidelberg (2014)
Perronnin, F., Sánchez, J., Mensink, T.: Improving the Fisher Kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15561-1_11
Sanchez, J., Perronnin, F., Mensink, T., Verbeek, J.: Image classification with the fisher vector: theory and practice. Int. J. Comput. Vis. 105(3), 222–245 (2013)
Sivapalan, S., Chen, D., Denman, S., Sridharan, S., Fookes, C.: Gait energy volumes and frontal gait recognition using depth images. In: IEEE IJCB, pp. 1–6 (2011)
Sivapalan, S., Chen, D., Denman, S., Sridharan, S., Fookes, C.: Histogram of weighted local directions for gait recognition. In: IEEE CVPR Workshop, pp. 125–130 (2013)
Sun, C., Nevatia, R.: Large-scale web video event classification by use of fisher vectors. In: IEEE WACV, pp. 15–22 (2013)
Wang, C., Zhang, J., Wang, L., Pu, J., Yuan, X.: Human identification using temporal information preserving gait template. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2164–2176 (2012)
Wang, H., Schmid, C.: Action recognition with improved trajectories. In: IEEE ICCV, pp. 3551–3558 (2013)
Wang, L., Ning, H., Tan, T., Hu, W.: Fusion of static and dynamic body biometrics for gait recognition. IEEE Trans. Circuits Syst. Video Technol. 14(2), 149–158 (2004)
Wang, L., Tan, T., Hu, W., Ning, H.: Automatic gait recognition based on statistical shape analysis. IEEE Trans. Image Process. 12(9), 1120–1131 (2003)
Wang, L., Tan, T., Ning, H., Hu, W.: Silhouette analysis-based gait recognition for human identification. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1505–1518 (2003)
Whytock, T., Belyaev, A., Robertson, N.M.: Dynamic distance-based shape features for gait recognition. J. Math. Imaging Vis. 50(3), 314–326 (2014)
Yang, Y., Tu, D., Li, G.: Gait recognition using flow histogram energy image. In: IEEE ICPR, pp. 444–449. IEEE (2014)
Zeng, W., Wang, C., Yang, F.: Silhouette-based gait recognition via deterministic learning. Pattern Recognit. 47(11), 3568–3584 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Khan, M.H., Li, F., Farid, M.S., Grzegorzek, M. (2018). Gait Recognition Using Motion Trajectory Analysis. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-59162-9_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59161-2
Online ISBN: 978-3-319-59162-9
eBook Packages: EngineeringEngineering (R0)