Abstract
In this paper a framework “Temporal-Vector Trajectory Learning” (TVTL) for human action recognition is proposed. In this framework, the major concept is that we would like to add the temporal information into the action recognition process. Base on this purpose, there are three kinds of temporal information, LTM, DTM, and TTM, being proposed. With the three kinds of proposed temporal information, the k-NN classifier based on the Mahanalobis distance metric do have better results than just using spatial information. The experimental results demonstrate that the method can recognize the actions well. Especially with our TTM and DTM framework, they do have great accuracy rates. Even with noisy data, the framework still have good performance.
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References
Ali, S., Basharat, A., Shah, M.: Chaotic invariants for human action recognition. In: ICCV, pp. 1–8 (2007)
Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in Neural Information Processing Systems 14, 585–591 (2002)
Bissacco, A., Chiuso, A., Ma, Y., Soatto, S.: Recognition of human gaits. CVPR 2, 52–57 (2001)
Bobick, A., Davis, J.: The recognition of human movement using temporal templates. PAMI 23(3), 257–267 (2001)
Bregler, C.: Learning and recognizing human dynamics in video sequences. In: CVPR, pp. 568–574 (1997)
Cai, D., He, X., Zhou, K., Han, J., Bao, H.: Locality sensitive discriminant analysis. In: IJCAI, pp. 708–713 (2007)
Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: ICCV Work-shop: VSPETS, pp. 65–72 (2005)
Efros, A., Berg, A., Mori, G., Malik, J.: Recognizing action at a distance. In: ICCV, vol. 2, pp. 726–733 (2003)
Elgammal, A., Lee, C.S.: Inferring 3D body pose from silhouettes using activity manifold learning. In: CVPR, vol. 2, pp. 681–688 (2004)
Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Action as space-time shapes. PAMI 29(12), 2247–2253 (2007)
He, X., Niyogi, P.: Locality preserving projections. Advances in Neural Information Processing Systems 16, 153–160 (2003)
Jia, L.K., Yeung, D.Y.: Human Action Recognition Using Local Spatio-Temporal Discriminant Embedding. In: CVPR, pp. 1–8 (2008)
Ke, Y., Sukthankar, R., Hebert, M.: Efficient visual event detection using volumetric features. In: ICCV, pp. 166–173 (2005)
Laptev, I.: On space-time interest points. IJCV 64(2-3), 107–123 (2005)
Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: CVPR, pp. 1–8 (2008)
Lv, F., Nevatia, R.: Single view human action recognition using key pose matching and Viterbi path searching. In: CVPR, pp. 1–8 (2007)
Niebles, J.C., Wang, H., Fei-Fei, L.: Unsupervised learn-ing of human action categories using spatial-temporal words. In: BMVC (2006)
Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 22, 290(5500), 2323–2326 (2000)
Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: ICPR, vol. 3, pp. 32–36 (2004)
Tenenbaum, J.B., Silva, V.D., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 22, 290(5500), 2319–2323 (2000)
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)
Wang, L., Ning, H.Z., Tan, T.N., Hu, W.M.: Fusion of static and dynamic body biometrics for gait recognition. In: ICCV, pp. 1449–1454 (2003)
Wang, L., Suter, D.: Recognizing human activities from silhouettes: motion subspace and factorial discriminative graphical model. In: CVPR, pp. 1–8 (2007)
Wang, L., Suter, D.: Learning and matching of dynamic shape manifolds for human action recognition. IEEE Trans. on IP 16(6), 1646–1661 (2007)
Wang, L., Suter, D.: Visual Learning and Recognition of Sequential Data Manifolds with Applications to Human Movement Analysis. CVIU 110(2), 153–172 (2008)
Weinberger, K.Q., Saul, L.K.: Distance Metric Learning for Large Margin Nearest Neighbor Classification. Journal of Machine Learning Research 10, 209–244 (2009)
Yacoob, Y., Black, M.J.: Parameterized modeling and recognition of activities. CVIU 73(2), 232–247 (1999)
Yan, S., Xu, D., Zhang, B., Zhang, H.J.: Graph Embedding and Extensions: A General Framework for Dimensionality Reduction. IEEE Trans. on PAMI 29(1), 40–51 (2007)
Zelnik-Manor, L., Irani, M.: Event-based analysis of video. CVPR 2, 123–130 (2001)
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Fang, CH., Chen, JC., Tseng, CC., Lien, JJ.J. (2010). Human Action Recognition Using Spatio-temporal Classification. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12304-7_10
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DOI: https://doi.org/10.1007/978-3-642-12304-7_10
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