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Multi-model particle filter-based tracking with switching dynamical state to study bedload transport

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Abstract

Multi-object tracking is a difficult problem underlying many computer vision applications. In this work, we focus on bedload sediment transport experiments in a turbulent flow were sediments are represented by small spherical calibrated glass beads. The aim is to track all beads over long time sequences to obtain sediment velocities and concentration. Classical algorithms used in fluid mechanics fail to track the beads over long sequences with a high precision because they incorrectly handle both miss-detections and detector imprecision. Our contribution is to propose a particle filter-based algorithm including a multiple motion model adapted to our problem. Additionally, this algorithm includes several improvements such as the estimation of the detector confidence to account for the lack of precision of the detector. The evaluation was made using two test sequences—one from our experimental setup and one from a simulation created numerically—with their dedicated ground truths. The results show that this algorithm outperforms state-of-the-art concurrent algorithms.

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Notes

  1. An application is online when the inputs to the associated algorithm is provided as a stream, piece-by-piece.

  2. https://perso.univ-st-etienne.fr/ducottet/.

  3. A mismatch is a mistaken switch of tracker identifiers.

  4. The precision is the fraction of retrieved instances that are relevant.

  5. The recall is the fraction of relevant instances that are retrieved.

References

  1. Ali, S., Shah, M.: Floor fields for tracking in high density crowd scenes. In: European Conference on Computer Vision (ECCV), pp. 1–14. Springer (2008). https://doi.org/10.1007/978-3-540-88688-4_1

  2. Andriluka, M., Roth, S., Schiele, B.: People-tracking-by-detection and people-detection-by-tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008). https://doi.org/10.1109/CVPR.2008.4587583

  3. Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50(2), 174–188 (2002). https://doi.org/10.1109/78.978374

    Article  Google Scholar 

  4. Arulampalam, M.S., Ristic, B., Gordon, N., Mansell, T.: Bearings-only tracking of manoeuvring targets using particle filters. J. Adv. Signal Process. EURASIP 2004(15), 1–15 (2004). https://doi.org/10.1155/S1110865704405095

    Article  MATH  Google Scholar 

  5. Bacchi, V., Recking, A., Eckert, N., Frey, P., Piton, G., Naaim, M.: The effects of kinetic sorting on sediment mobility on steep slopes. Earth Surf. Process. Landf. 39(8), 1075–1086 (2014). https://doi.org/10.1002/esp.3564

    Article  Google Scholar 

  6. Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the CLEAR MOT metrics. J. Image Video Process. 2008(246309), 1–10 (2008). https://doi.org/10.1155/2008/246309

    Google Scholar 

  7. Blom, H.A.P., Bar-Shalom, Y.: The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Trans. Autom. Control 33(8), 780–783 (1988). https://doi.org/10.1109/9.1299

    Article  MATH  Google Scholar 

  8. Boers, Y., Driessen, J.N.: Interacting multiple model particle filter. IEE Proc. Radar Sonar Navig. 150(5), 344–349 (2003). https://doi.org/10.1049/ip-rsn:20030741

    Article  Google Scholar 

  9. Böhm, T., Frey, P., Ducottet, C., Ancey, C., Jodeau, M., Reboud, J.L.: Two-dimensional motion of a set of particles in a free surface flow with image processing. Exp. Fluids 41(1), 1–11 (2006). https://doi.org/10.1007/s00348-006-0134-9

    Article  Google Scholar 

  10. Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Van Gool, L.: Online multiperson tracking-by-detection from a single, uncalibrated camera. IEEE Trans. Pattern Anal. Mach. Intell (TPAMI) 33(9), 1820–1833 (2011). https://doi.org/10.1109/TPAMI.2010.232

    Article  Google Scholar 

  11. Chang, D.C., Fan, M.W.: Interacting multiple model particle filtering using new particle resampling algorithm. In: Global Communications Conference (GLOBECOM), pp. 3215–3219. IEEE (2014). https://doi.org/10.1109/glocom.2014.7037301

  12. Choset, H., Nagatani, K.: Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization. IEEE Trans. Robot. Autom. 17(2), 125–137 (2001). https://doi.org/10.1109/70.928558

    Article  Google Scholar 

  13. Dou, J., Li, J.: Robust visual tracking based on interactive multiple model particle filter by integrating multiple cues. Neurocomputing 135, 118–129 (2014). https://doi.org/10.1016/j.neucom.2013.12.049

    Article  Google Scholar 

  14. Doucet, A., de Freitas, A., Gordon, N.: Sequential Monte Carlo methods in practice. Springer Science & Business Media (2001). https://doi.org/10.1007/978-1-4757-3437-9

  15. Efron, B., Tibshirani, R.J.: An introduction to the bootstrap. CRC Press, Boca Raton (1994). https://doi.org/10.1007/978-1-4899-4541-9

    MATH  Google Scholar 

  16. Frey, P.: Particle velocity and concentration profiles in bedload experiments on a steep slope. Earth Surf. Process. Landf. 39(5), 646–655 (2014). https://doi.org/10.1002/esp.3517

    Article  Google Scholar 

  17. Gordon, N.J., Salmond, D.J., Smith, A.F.M.: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc. F Radar Signal Process. 140(2), 107–113 (1993). https://doi.org/10.1049/ip-f-2.1993.0015

    Article  Google Scholar 

  18. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: European Conference on Computer Vision (ECCV), vol. 7575, pp. 702–715. Springer (2012). https://doi.org/10.1007/978-3-642-33765-9_50

  19. Hergault, V., Frey, P., Métivier, F., Barat, C., Ducottet, C., Böhm, T., Ancey, C.: Image processing for the study of bedload transport of two-size spherical particles in a supercritical flow. Exp. Fluids 49(5), 1095–1107 (2010). https://doi.org/10.1007/s00348-010-0856-6

    Article  Google Scholar 

  20. Honkanen, M., Nobach, H.: Background extraction from double-frame PIV images. Exp. Fluids 38(3), 348–362 (2005). https://doi.org/10.1007/s00348-004-0916-x

    Article  Google Scholar 

  21. Houssais, M., Ortiz, C.P., Durian, D.J., Jerolmack, D.J.: Onset of sediment transport is a continuous transition driven by fluid shear and granular creep. Nat. Commun. 6, 6527 (2015). https://doi.org/10.1038/ncomms7527

    Article  Google Scholar 

  22. Isard, M., Blake, A.: Condensation-conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998). https://doi.org/10.1023/A:1008078328650

    Article  Google Scholar 

  23. Isard, M., Blake, A.: A mixed-state condensation tracker with automatic model-switching. In: IEEE 6th International Conference on Computer Vision (ICCV), pp. 107–112. IEEE (1998). https://doi.org/10.1109/ICCV.1998.710707

  24. Khalid, S.S., Abrar, S.: A low-complexity interacting multiple model filter for maneuvering target tracking. AEU-Int. J. Electron. Commun. 73, 157–164 (2017). https://doi.org/10.1016/j.aeue.2017.01.011

    Article  Google Scholar 

  25. Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logist. Q. 2(1–2), 83–97 (1955). https://doi.org/10.1007/978-3-540-68279-0_2

    Article  MathSciNet  MATH  Google Scholar 

  26. Lafaye de Micheaux, H., Ducottet, C., Frey, P.: Online multi-model particle filter-based tracking to study bedload transport. In: IEEE International Conference on Image Processing (ICIP), pp. 3489–3493. IEEE (2016). https://doi.org/10.1109/ICIP.2016.7533008

  27. Leibe, B., Schindler, K., Cornelis, N., Van Gool, L.: Coupled object detection and tracking from static cameras and moving vehicles. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 30(10), 1683–1698 (2008). https://doi.org/10.1109/TPAMI.2008.170

    Article  Google Scholar 

  28. Li, X.R., Jilkov, V.P.: Survey of maneuvering target tracking. Part V. Multiple-model methods. IEEE Trans. Aerosp. Electron. Syst. 41(4), 1255–1321 (2005). https://doi.org/10.1109/taes.2005.1561886

    Article  Google Scholar 

  29. Lu, W.L., Okuma, K., Little, J.J.: Tracking and recognizing actions of multiple hockey players using the boosted particle filter. Image Vis. Comput. 27(1), 189–205 (2009). https://doi.org/10.1016/j.imavis.2008.02.008

    Article  Google Scholar 

  30. Maurin, R., Chauchat, J., Chareyre, B., Frey, P.: A minimal coupled fluid-discrete element model for bedload transport. Phys. Fluids (1994-present) 27(11), 113,302 (2015). https://doi.org/10.1063/1.4935703

  31. Maurin, R., Chauchat, J., Frey, P.: Dense granular flow rheology in turbulent bedload transport. J. Fluid Mech. 804, 490–512 (2016). https://doi.org/10.1017/jfm.2016.520

    Article  MathSciNet  Google Scholar 

  32. Maurin, R., Chauchat, J., Frey, P.: Revisiting slope influence in turbulent bedload transport: Consequences for vertical flow structure and transport rate scaling. J. Fluid Mech. 839, 135–156 (2018). https://doi.org/10.1017/jfm.2017.903

    Article  MathSciNet  Google Scholar 

  33. Mazor, E., Averbuch, A., Bar-Shalom, Y., Dayan, J.: Interacting multiple model methods in target tracking: a survey. IEEE Trans. Aerosp. Electron. Syst. 34(1), 103–123 (1998). https://doi.org/10.1109/7.640267

    Article  Google Scholar 

  34. McGinnity, S., Irwin, G.W.: Multiple model bootstrap filter for maneuvering target tracking. IEEE Trans. Aerosp. Electron. Syst. 36(3), 1006–1012 (2000). https://doi.org/10.1109/7.869522

    Article  Google Scholar 

  35. Odobez, J.M., Gatica-Perez, D., Ba, S.O.: Embedding motion in model-based stochastic tracking. IEEE Trans. Image Proces. 15(11), 3514–3530 (2006). https://doi.org/10.1109/TIP.2006.877497

    Article  Google Scholar 

  36. Ohmi, K., Li, H.Y.: Particle-tracking velocimetry with new algorithms. Meas. Sci. Technol. 11(6), 603–616 (2000). https://doi.org/10.1088/0957-0233/11/6/303

    Article  Google Scholar 

  37. Ouellette, N.T., Xu, H., Bodenschatz, E.: A quantitative study of three-dimensional Lagrangian particle tracking algorithms. Exp. Fluids 40(2), 301–313 (2006). https://doi.org/10.1007/s00348-005-0068-7

    Article  Google Scholar 

  38. Rathi, Y., Vaswani, N., Tannenbaum, A., Yezzi, A.: Tracking deforming objects using particle filtering for geometric active contours. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 29(8), 1470–1475 (2007). https://doi.org/10.1109/TPAMI.2007.1081

    Article  Google Scholar 

  39. Recking, A., Bacchi, V., Naaim, M., Frey, P.: Antidunes on steep slopes. J. Geophys. Res. Earth Surf. 114, F04025 (2009). https://doi.org/10.1029/2008JF001216

    Google Scholar 

  40. Smeulders, A.W.M., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: An experimental survey. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 36(7), 1442–1468 (2014). https://doi.org/10.1109/TPAMI.2013.230

    Article  Google Scholar 

  41. Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Berlin (1999). https://doi.org/10.1007/978-3-662-05088-0

    Book  MATH  Google Scholar 

  42. Theunissen, R., Scarano, F., Riethmuller, M.: On improvement of PIV image interrogation near stationary interfaces. Exp. Fluids 45(4), 557–572 (2008). https://doi.org/10.1007/s00348-008-0481-9

    Article  Google Scholar 

  43. Westerweel, J., Elsinga, G.E., Adrian, R.J.: Particle image velocimetry for complex and turbulent flows. Ann. Rev. Fluid Mech. 45(1), 409–436 (2013). https://doi.org/10.1146/annurev-fluid-120710-101204

    Article  MathSciNet  MATH  Google Scholar 

  44. Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. Int. J. Comput. Vis. 75(2), 247–266 (2007). https://doi.org/10.1007/s11263-006-0027-7

    Article  Google Scholar 

  45. Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2411–2418. IEEE (2013). https://doi.org/10.1109/CVPR.2013.312

  46. Zhai, Y., Yeary, M.B., Cheng, S., Kehtarnavaz, N.: An object-tracking algorithm based on multiple-model particle filtering with state partitioning. IEEE Trans. Instrum. Meas. 58(5), 1797–1809 (2009). https://doi.org/10.1109/tim.2009.2014511

    Article  Google Scholar 

  47. Zhan, B., Monekosso, D.N., Remagnino, P., Velastin, S.A., Xu, L.Q.: Crowd analysis: a survey. Mach. Vis. Appl. 19(5), 345–357 (2008). https://doi.org/10.1007/s00138-008-0132-4

    Article  Google Scholar 

  48. Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008). https://doi.org/10.1109/CVPR.2008.4587584

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Acknowledgements

This research was supported by Irstea; labex OSUG@2020; INSU programme EC2CO-BIOHEFECT and DRIL Modsed; the French national research agency project SegSed ANR-16-CE01-0005; and the Rhône-Alpes region as part of its higher education, research and innovation regional Strategy (Environment Academic Research Community).

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Authors and Affiliations

  1. Laboratoire Hubert Curien UMR 5516, UJM-Saint-Étienne, CNRS, IOGS, Université de Lyon, 42023, Saint-Étienne, France

    Hugo Lafaye de Micheaux & Christophe Ducottet

  2. Université Grenoble Alpes, Irstea, UR ETGR, 2 rue de la Papeterie-BP 76, 38402 , St-Martin-d’Hères, France

    Hugo Lafaye de Micheaux & Philippe Frey

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  1. Hugo Lafaye de Micheaux
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  2. Christophe Ducottet
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  3. Philippe Frey
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Correspondence to Hugo Lafaye de Micheaux.

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Lafaye de Micheaux, H., Ducottet, C. & Frey, P. Multi-model particle filter-based tracking with switching dynamical state to study bedload transport. Machine Vision and Applications 29, 735–747 (2018). https://doi.org/10.1007/s00138-018-0925-z

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