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Bidirectional Markov Chain Monte Carlo Particle Filter for Articulated Human Motion Tracking

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Digital TV and Wireless Multimedia Communication (IFTC 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 815))

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Abstract

A novel framework of particle filter, named bidirectional Markov chain Monte Carlo particle filter (BMCMCPF), has been proposed to estimate articulated human movement state and action category jointly. Owing to the reason that we regard action category as the estimated state in our framework, firstly the motion models for every possible action are built via autoregressive modeling for the captured motion data with minimum distance. Meanwhile, the dynamic model and observation model also get coupled so that tracking and recognition can achieve synchronously. Then, the state estimation is completed by using the bidirectional Marko chain Monte Carlo sampling. BMCMCPF can not only improve the tracking performance as its global optimization property, but also smooth the joint’s movement trajectories to ensure the motion coordination. The experimental results on HumanEva datasets show that the effectiveness of BMCMCPF with unknown motion modality in solving the tracking problem.

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References

  1. Li, X.R., Jilkov, V.P.: Survey of maneuvering target tracking. Part I. Dynamic models. IEEE Trans. Aerosp. Electron. Syst. 39(4), 1333–1364 (2004)

    Google Scholar 

  2. Lin, S.Y., Chang, I.: 3D human motion tracking using progressive particle filter. Pattern Recogn. 43(10), 3621–3635 (2010)

    Article  MATH  Google Scholar 

  3. Lin, S.-Y., Chang, I.-C.: Dynamic kernel-based progressive particle filter for 3D human motion tracking. In: Zha, H., Taniguchi, R.-i., Maybank, S. (eds.) ACCV 2009. LNCS, vol. 5995, pp. 257–266. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12304-7_25

    Chapter  Google Scholar 

  4. Shi, X.G.: 3D Human Motion Tracking Based on Single Video Input and Particle Filtering. National Chung Cheng University, Taiwan (2013)

    Google Scholar 

  5. Fleet, D.J.: Motion models for people tracking. In: Moeslund, T., Hilton, A., Krüger, V., Sigal, L. (eds.) Visual Analysis of Humans, pp. 171–198. Springer, London (2011). https://doi.org/10.1007/978-0-85729-997-0_10

    Chapter  Google Scholar 

  6. Agarwal, A., Triggs, B.: Tracking articulated motion using a mixture of autoregressive models. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3023, pp. 54–65. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24672-5_5

    Chapter  Google Scholar 

  7. Lan, S.-F., Ho, M.-F., Huang, C.-L.: Human motion parameter capturing using particle filter and nonparametric belief propagation. In: IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 37–40 (2008)

    Google Scholar 

  8. Saboune, J., Rose, C., Charpillet, F.: Factored interval particle filtering for gait analysis. In: IEEE Engineering in Medicine and Biology Society, pp. 3232–3235 (2007)

    Google Scholar 

  9. Gonczarek, A., Tomczak, J.M.: Articulated tracking with manifold regularized particle filter. Mach. Vis. Appl. 27(2), 275–286 (2016)

    Article  MATH  Google Scholar 

  10. Li, R., Tian, T.P., Sclaroff, S., et al.: 3D human motion tracking with a coordinated mixture of factor analyzers. Int. J. Comput. Vis. 87(1), 170 (2010)

    Article  Google Scholar 

  11. Blom, H.A.P., Barshalom, Y.: The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Trans. Autom. Control 33(8), 780–783 (1988)

    Article  MATH  Google Scholar 

  12. Madrigal, F., Hayet, J.B.: Evaluation of multiple motion models for multiple pedestrian visual tracking. In: IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 31–36 (2013)

    Google Scholar 

  13. Taylor, G.W., Sigal, L., Fleet, D.J., et al.: Dynamical binary latent variable models for 3D human pose tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 631–638 (2010)

    Google Scholar 

  14. Khalili, A., Soliman, A.A., Asaduzzaman, M.: Quantum particle filter: a multiple mode method for low delay abrupt pedestrian motion tracking. Electron. Lett. 51(16), 1251–1253 (2015)

    Article  Google Scholar 

  15. Sigal, L., Black, M.J.: HumanEva: Synchronized Video and Motion Capture Dataset for Evaluation of Articulated Human Motion. Int. J. Comput. Vision 87(1–2), 4–27 (2006)

    Google Scholar 

  16. Daubney, B., Xie, X.: Tracking 3D human pose with large root node uncertainty. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1321–1328. IEEE Computer Society (2011)

    Google Scholar 

  17. Lei, J., Li, G., Li, S., et al.: Continuous action recognition based on hybrid CNN-LDCRF model. In: International Conference on Image, Vision and Computing, pp. 63–69 (2016)

    Google Scholar 

  18. Peursum, P., Venkatesh, S., West, G.: A study on smoothing for particle-filtered 3D human body tracking. Int. J. Comput. Vis. 87(1–2), 53–74 (2010)

    Article  Google Scholar 

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Acknowledgment

This work is supported by the National Natural Science Foundation of China under Grant Nos. 61201236 and 61371191, and the Project of State Administration of Press, Publication, Radio, Film and Television under Grant No. 2015-53.

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Correspondence to Long Ye .

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Yu, A., Li, C., Ye, L., Wang, J., Zhang, Q. (2018). Bidirectional Markov Chain Monte Carlo Particle Filter for Articulated Human Motion Tracking. In: Zhai, G., Zhou, J., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2017. Communications in Computer and Information Science, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-10-8108-8_38

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  • DOI: https://doi.org/10.1007/978-981-10-8108-8_38

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8107-1

  • Online ISBN: 978-981-10-8108-8

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