Abstract
In this paper, we propose a novel algorithm for articulated object tracking, based on a hierarchical search and particle swarm optimization. Our approach aims to reduce the complexity induced by the high dimensional state space in articulated object tracking by decomposing the search space into subspaces and then using particle swarms to optimize over these subspaces hierarchically. Moreover, the intelligent search strategy proposed in [20] is integrated into each optimization step to provide a robust tracking algorithm under noisy observation conditions. Our quantitative and qualitative analysis both on synthetic and real video sequences show the efficiency of the proposed approach compared to other existing competitive tracking methods.
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References
Balan, A.O., Sigal, L., Black, M.J.: A quantitative evaluation of video-based 3d person tracking. In: PETS, pp. 349–356 (2005)
Bandouch, J., Engstler, F., Beetz, M.: Evaluation of Hierarchical Sampling Strategies in 3D Human Pose Estimation. In: BMVC, pp. 925–934 (2008)
Bray, M., Kollermeier, E., Vangool, L.: Smart particle filtering for high-dimensional tracking. Computer Vision and Image Understanding 106(1), 116–129 (2007)
Brubaker, M., Fleet, D., Hertzmann, A.: Physics-based person tracking using the anthropomorphic walker. International Journal of Computer Vision 87(1-2), 140–155 (2009)
Chang, I.C., Lin, S.Y.: 3D human motion tracking based on a progressive particle filter. Pattern Recognition 43(10), 3621–3635 (2010)
Deutscher, J., Reid, I.: Articulated body motion capture by stochastic search. International Journal of Computer Vision 61(2), 185–205 (2005)
Hauberg, S., Pedersen, K.S.: Stick it! articulated tracking using spatial rigid object priors. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part III. LNCS, vol. 6494, pp. 758–769. Springer, Heidelberg (2011)
Hofmann, M., Gavrila, D.: 3D human model adaptation by frame selection and shape-texture optimization. Computer Vision and Image Understanding 115(11), 1559–1570 (2011)
Isard, M.: PAMPAS: real-valued graphical models for computer vision. In: CVPR, pp. 613–620 (2003)
John, V., Trucco, E., Ivekovic, S.: Markerless human articulated tracking using hierarchical particle swarm optimization. Image and Vision Computing 28(11), 1530–1547 (2010)
Kjellstrom, H., Kragic, D., Black, M.: Tracking people interacting with objects. In: CVPR, pp. 747–754 (2010)
MacCormick, J., Isard, M.: Partitioned sampling, articulated objects, and interface-quality hand tracking. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 3–19. Springer, Heidelberg (2000)
Oikonomidis, I., Kyriazis, N.: Full DOF tracking of a hand interacting with an object by modeling occlusions and physical constraints. In: ICCV, pp. 2088–2095 (2011)
Rose, C., Saboune, J., Charpillet, F.: Reducing particle filtering complexity for 3D motion capture using dynamic Bayesian networks, 1396–1401. AAAI (2008)
Sigal, L., Balan, R.: Humaneva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. Technical report (2009)
Urtasun, R., Fleet, D., Hertzmann, A., Fua, P.: Priors for people tracking from small training sets. In: ICCV, pp. 403–410 (2005)
Wu, Y., Hua, G., Yu, T.: Tracking articulated body by dynamic Markov network. In: ICCV, pp. 1094–1101 (2003)
Xinyu, X., Baoxin, L.: Learning Motion Correlation for Tracking Articulated Human Body with a Rao-Blackwellised Particle Filter. In: ICCV, pp. 1–8 (2007)
Yao, A., Gall, J., Gool, L., Urtasun, R.: Learning probabilistic non-linear latent variable models for tracking complex activities. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K. (eds.) Advances in Neural Information Processing Systems 24, pp. 1359–1367 (2011)
Zhang, X., Hu, W., Wang, X., Kong, Y., Xie, N., Wang, H., Ling, H., Maybank, S.: A swarm intelligence based searching strategy for articulated 3D human body tracking. In: CVPRW, pp. 45–50 (2010)
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Nguyen, X.S., Dubuisson, S., Gonzales, C. (2013). Hierarchical Annealed Particle Swarm Optimization for Articulated Object Tracking. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_38
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DOI: https://doi.org/10.1007/978-3-642-40261-6_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40260-9
Online ISBN: 978-3-642-40261-6
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