Abstract
Understanding human motion in unconstrained 2D videos has been a central theme in Computer Vision research, and over the years many attempts have been made to design effective representations of video content. In this paper, we apply to gait recognition the Motion Interchange Patterns (MIP) framework, a 3D extension of the LBP descriptors to videos that was successfully employed in action recognition. This effective framework encodes motion by capturing local changes in motion directions. Our scheme does not rely on silhouettes commonly used in gait recognition, and benefits from the capability of MIP encoding to model real world videos. We empirically demonstrate the effectiveness of this modeling of human motion on several challenging gait recognition datasets.
Chapter PDF
References
Ali, S., Shah, M.: Human action recognition in videos using kinematic features and multiple instance learning. TPAMI 32(2), 288–303 (2010)
Bashir, K., Xiang, T., Gong, S.: Cross view gait recognition using correlation strength. In: BMVC, pp. 1–11 (2010)
Bashir, K., Xiang, T., Gong, S., Mary, Q.: Gait representation using flow fields. In: BMVC, pp. 1–11 (2009)
Chen, C., Liang, J., Zhao, H., Hu, H., Tian, J.: Frame difference energy image for gait recognition with incomplete silhouettes. Pattern Recognition Letters 30(11), 977–984 (2009)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, CVPR. vol. 1, pp. 886–893. IEEE (2005)
DeCann, B., Ross, A.: Gait curves for human recognition, backpack detection, and silhouette correction in a nighttime environment. In: International Society for Optics and Photonics, SPIE Defense, Security, and Sensing, pp. 76670Q–76670Q (2010)
Efros, A.A., Berg, A.C., Mori, G., Malik, J.: Recognizing action at a distance. In: Proceedings of Ninth IEEE International Conference on Computer Vision, 2003, pp. 726–733 (2003)
Fathi, A., Mori, G.: Action recognition by learning mid-level motion features. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008, CVPR, pp. 1–8 (2008)
Gong, W., Sapienza, M., Cuzzolin, F.: Fisher tensor decomposition for unconstrained gait recognition. Training 2 3 (2013)
Guan, Y., Wei, X., Li, C.T., Marcialis, G.L., Roli, F., Tistarelli, M.: Combining gait and face for tackling the elapsed time challenges. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–8. IEEE (2013)
Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(2), 316–322 (2006)
Hassner, T., Itcher, Y., Kliper-Gross, O.: Violent flows: Real-time detection of violent crowd behavior. In: 2012 IEEE Computer Society Conference on CVPRW, pp. 1–6. IEEE (2012)
Hofmann, M., Bachmann, S., Rigoll, G.: 2.5 d gait biometrics using the depth gradient histogram energy image. In: 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 399–403. IEEE (2012)
Hofmann, M., Geiger, J., Bachmann, S., Schuller, B., Rigoll, G.: The tum gait from audio, image and depth (gaid) database: Multimodal recognition of subjects and traits. Journal of Visual Communication and Image Representation 25(1), 195–206 (2014)
Hofmann, M., Rigoll, G.: Improved gait recognition using gradient histogram energy image. In: 2012 19th IEEE International Conference on Image Processing (ICIP), pp. 1389–1392. IEEE (2012)
Hu, M., Wang, Y., Zhang, Z., Zhang, D., Little, J.J.: Incremental learning for video-based gait recognition with lbp flow. IEEE Transactions on Cybernetics 43(1), 77–89 (2013)
Iwashita, Y., Uchino, K., Kurazume, R.: Gait-based person identification robust to changes in appearance. Sensors 13(6), 7884–7901 (2013)
Ke, Y., Sukthankar, R., Hebert, M.: Efficient visual event detection using volumetric features. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 1, pp. 166–173. IEEE (2005)
Kellokumpu, V., Zhao, G., Li, S.Z., Pietikäinen, M.: Dynamic texture based gait recognition. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 1000–1009. Springer, Heidelberg (2009)
Kellokumpu, V., Zhao, G., Pietikäinen, M.: Human activity recognition using a dynamic texture based method. BMVC 1, 2 (2008)
Kliper-Gross, O., Gurovich, Y., Hassner, T., Wolf, L.: Motion interchange patterns for action recognition in unconstrained videos. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 256–269. Springer, Heidelberg (2012)
Kovashka, A., Grauman, K.: Learning a hierarchy of discriminative space-time neighborhood features for human action recognition. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2046–2053 (2010)
Kusakunniran, W., Wu, Q., Li, H., Zhang, J.: Automatic gait recognition using weighted binary pattern on video. In: Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2009, pp. 49–54 (2009)
Kusakunniran, W., Wu, Q., Zhang, J., Li, H.: Pairwise shape configuration-based psa for gait recognition under small viewing angle change. In: 2011 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pp. 17–22. IEEE (2011)
Lam, T.H., Cheung, K.H., Liu, J.N.: Gait flow image: A silhouette-based gait representation for human identification. Pattern recognition 44(4), 973–987 (2011)
Laptev, I.: On space-time interest points. International Journal of Computer Vision 64(2–3), 107–123 (2005)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178. IEEE (2006)
Liu, J., Yang, Y., Saleemi, I., Shah, M.: Learning semantic features for action recognition via diffusion maps. Computer Vision and Image Understanding 116(3), 361–377 (2012)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Qin, J., Luo, T., Shao, W., Chung, R., Chow, K.: A bag-of-gait model for gait recognition
Schindler, K., Van Gool, L.: Action snippets: How many frames does human action recognition require? In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)
Sivapalan, S., Chen, D., Denman, S., Sridharan, S., Fookes, C.: Histogram of weighted local directions for gait recognition. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 125–130. IEEE (2013)
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. IEEE (2006)
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)
Whytock, T., Belyaev, A., Robertson, N.M.: Dynamic distance-based shape features for gait recognition. Journal of Mathematical Imaging and Vision, pp. 1–13 (2014)
Yeffet, L., Wolf, L.: Local trinary patterns for human action recognition. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 492–497. IEEE (2009)
Yogarajah, P., Condell, J.V., Prasad, G.: P rw gei: Poisson random walk based gait recognition. In: 2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 662–667. IEEE (2011)
Yu, S., Tan, D., Huang, K., Tan, T.: Reducing the effect of noise on human contour in gait recognition. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 338–346. Springer, Heidelberg (2007)
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. IEEE (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Freidlin, G., Levy, N., Wolf, L. (2015). Gait-Based Person Identification Using Motion Interchange Patterns. In: Agapito, L., Bronstein, M., Rother, C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science(), vol 8926. Springer, Cham. https://doi.org/10.1007/978-3-319-16181-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-16181-5_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16180-8
Online ISBN: 978-3-319-16181-5
eBook Packages: Computer ScienceComputer Science (R0)