Abstract
Except for gait analysis in a controlled environment, few have considered the use of motion characteristics for human identification, due to the complexity caused by the spatial nonrigidity and temporal randomness of human action. This work is a new attempt at mining biometric information from more general actions. A novel method for calculating the distance between two time series is proposed, where automatic segmentation and matching are conducted simultaneously. Given a query sequence, our method can efficiently match it against the gallery dataset. Local continuity and global optimality are both considered. The matching algorithm is efficiently solved by Linear Programming (LP). Synthetic data sequences and challenging broadcast sports videos are used to validate the effectiveness of our algorithm. The results show that action-based biometrics are promising for human identification, and the proposed approach is effective for this application.
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References
Xu, D., Huang, Y., Zeng, Z., Xu, X.: Human gait recognition using patch distribution feature and locality-constrained group sparse representation. IEEE Trans. Image Process. 21, 316–326 (2011)
Venkat, I., Wilde, P.D.: Robust gait recognition by learning and exploiting sub-gait characteristics. Int. J. Comput. Vis. 91, 7–23 (2011)
Lam, T.H., Cheung, K., Liu, J.N.: Gait flow image: A silhouette-based gait representation for human identification. Pattern Recog. 44, 973–987 (2011)
Lu, W.L., Ting, J.A., Murphy, K.P., Little, J.J.: Identifying players in broadcast sports videos using conditional random fields. In: Proc. IEEE Comput. Vis. Pattern Recog., pp. 3249–3256 (2011)
Kusakunniran, W., Wu, Q., Zhang, J., Li, H.: Support vector regression for multi-view gait recognition based on local motion feature selection. In: Proc. IEEE Comput. Vis. Pattern Recog., pp. 974–981 (2010)
Hu, M., Wang, Y., Zhang, Z., Zhang, D.: Multi-view multi-stance gait identification. In: Proc. IEEE Int. Conf. Image Process., pp. 541–544 (2011)
Gheissari, N., Sebastian, T., Hartley, R.: Person reidentification using spatiotemporal appearance. In: Proc. IEEE Comput. Vis. Pattern Recog., vol. 2, pp. 1528–1535 (2006)
Zheng, W.S., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: Proc. IEEE Comput. Vis. Pattern Recog., pp. 649–656 (2011)
Yu, G., Yuan, J., Liu, Z.: Real-time human action search using random forest based hough voting. In: Proc. ACM Multimedia, pp. 1149–1152 (2011)
Müller, M.: Information Retrieval for Music and Motion. Springer-Verlag New York, Inc., Secaucus (2007)
Ikizler-Cinbis, N., Sclaroff, S.: Object, Scene and Actions: Combining Multiple Features for Human Action Recognition. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 494–507. Springer, Heidelberg (2010)
Benenson, R., Mathias, M., Timofte, R., Gool, L.V.: Pedestrian detection at 100 frames per second. In: Proc. IEEE Comput. Vis. Pattern Recog., pp. 1–8 (2012)
Gaur, U., Zhu, Y., Song, B., Roy-Chowdhury, A.: A ”string of feature graphs” model for recognition of complex activities in natural videos. In: Proc. IEEE Int. Conf. Comput. Vis., pp. 2595–2602 (2011)
Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B, Methodological 58, 267–288 (1996)
Li, H., Kim, E., Huang, X., He, L.: Object matching with a locally affine-invariant constraint. In: Proc. IEEE Comput. Vis. Pattern Recog., pp. 1641–1648 (2010)
Dantzig, G.B., Thapa, M.N.: Linear Programming 2: Theory and Extensions. Springer (2003)
Grant, M., Boyd, S.: CVX: Matlab software for disciplined convex programming, version 1.21 (2011), http://cvxr.com/cvx/
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Hu, M., Wang, Y., Little, J.J. (2013). Combinational Subsequence Matching for Human Identification from General Actions. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37431-9_35
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DOI: https://doi.org/10.1007/978-3-642-37431-9_35
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