access icon free Visual multiple-object tracking for unknown clutter rate

© The Institution of Engineering and Technology
Received 04/12/2017, Accepted 14/03/2018, Revised 23/02/2018, Published 16/03/2018

In multi-object tracking applications, model parameter tuning is a prerequisite for reliable performance. In particular, it is difficult to know statistics of false measurements due to various sensing conditions and changes in the field of views. In this study, the authors are interested in designing a multi-object tracking algorithm that handles unknown false measurement rate. The recently proposed robust multi-Bernoulli filter is employed for clutter estimation while generalised labelled multi-Bernoulli filter is considered for target tracking. Performance evaluation with real videos demonstrates the effectiveness of the tracking algorithm for real-world scenarios.

Inspec keywords: video signal processing; filtering theory; object tracking; target tracking; statistical analysis

Other keywords: robust multiBernoulli filter; model parameter tuning; field-of-views; unknown false measurement rate; unknown clutter rate; sensing conditions; generalised labelled multiBernoulli filter; clutter estimation; target tracking; visual multiple-object tracking

Subjects: Video signal processing; Optical, image and video signal processing; Other topics in statistics; Computer vision and image processing techniques; Other topics in statistics; Filtering methods in signal processing

References

    1. 1)
      • 10. Mahler, R.: ‘Multi-target Bayes filtering via first-order multi-target moments’, IEEE Trans. Aerosp. Electron. Syst., 2003, 39, (4), pp. 1152–1178.
    2. 2)
      • 26. Ferryman, J., Shahrokni, A.: ‘PETS2009: Dataset and challenge’. IEEE Workshop Performance Evaluation of Tracking and Surveillance, Snowbird, Utah, USA, 2009.
    3. 3)
      • 29. Dollár, P., Tu, Z., Perona, P., et al: ‘Integral channel features’. BMVC, London, UK, 2009.
    4. 4)
      • 17. Vo, B.-T., Vo, B.-N., Cantoni, A.: ‘The cardinality balanced multi-target multi-Bernoulli filter and its implementations’, IEEE Trans. Signal Process., 2009, 57, (2), pp. 409–423.
    5. 5)
      • 28. Chenouard, N., Bloch, I., Olivo-Marin, J.-C.: ‘Multiple hypothesis tracking for cluttered biological image sequences’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (11), pp. 2736–2750.
    6. 6)
      • 30. Dalal, N., Triggs, B.: ‘Histogram of oriented gradients for human detection’. Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), San Diego, California, USA, 2005.
    7. 7)
      • 25. Ess, A., Leibe, B., Gool, L.V.: ‘Depth and appearance for mobile scene analysis’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Minneapolis, Minnesota, USA, 2007.
    8. 8)
      • 6. Laet, T.D., Bruyninckx, H., Schutter, J.D.: ‘Shape-based online multitarget tracking and detection for targets causing multiple measurements: variational Bayesian clustering and lossless data association’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (12), pp. 2477–2491.
    9. 9)
      • 9. Milan, A., Roth, S., Schinlder, K.: ‘Continuous energy minimization for multitarget tracking’, IEEE Trans. Pattern Anal. Mach. Intell., 2014, 36, (1), pp. 58–72.
    10. 10)
      • 33. Wang, Q., Fang, J., Yuan, Y.: ‘Multi-cue based tracking’, Neurocomputing, 2014, 131, pp. 227–236.
    11. 11)
      • 12. Rezatofighi, S.H., Gould, S., Vo, B.-T., et al: ‘Multi-target tracking with time-varying clutter rate and detection profile: application to time-lapse cell microscopy sequences’, IEEE Trans. Med. Imaging, 2015, 34, (6), pp. 1336–1348.
    12. 12)
      • 4. Reid, D.: ‘An algorithm for tracking multiple targets’, IEEE Trans. Autom. Control, 1979, 24, (6), pp. 843–854.
    13. 13)
      • 18. Hoseinnezhad, R., Vo, B.-N., Vo, B.-T., et al: ‘Visual tracking of numerous targets via multi-Bernoulli filtering of image data’, Pattern Recognit., 2012, 45, (10), pp. 3625–3635.
    14. 14)
      • 13. Beard, M., Vo, B.-T., Vo, B.-N.: ‘Multi-target filtering with unknown clutter density using a bootstrap GM-CPHD filter’, IEEE Signal Process. Lett., 2013, 20, (4), pp. 323–326.
    15. 15)
      • 31. Henriques, J.F., Caseiro, R., Martins, P., et al: ‘High-Speed tracking with kernelized correlation filters’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 37, (3), pp. 583–596.
    16. 16)
      • 21. Schuhmacher, D., Vo, B.-T., Vo, B.-N.: ‘A consistent metric for performance evaluation of multi-object filters’. IEEE Trans. Signal Process., 2008, 56, (8), pp. 3447–3457.
    17. 17)
      • 2. Bar-Shalom, Y., Fortmann, T.E.: ‘Tracking and data Association’ (Academic Press, San Diego, 1998).
    18. 18)
      • 11. Mahler, R.: ‘PHD filters of higher order in target number’, IEEE Trans. Aerosp. Electron. Syst., 2007, 43, (3), pp. 1523–1543.
    19. 19)
      • 5. Breitenstein, M.D., Reichlin, F., Leibe, B., et al: ‘Online multiperson tracking-by-detection from a single, uncalibrated camera’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (9), pp. 1820–1833.
    20. 20)
      • 19. Vo, B.-T., Vo, B.-N.: ‘Labeled random finite sets and multi-object conjugate priors’, IEEE Trans. Signal Process., 2013, 61, (13), pp. 3460–3475.
    21. 21)
      • 24. Andriluka, M., Roth, S., Schiele, B.: ‘People-tracking-by-detection and people-detection-by-tracking’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Anchorage, Alaska, USA, 2008.
    22. 22)
      • 15. Vo, B.-T., Vo, B.-N., Hoseinnezhad, R., et al: ‘Robust multi-Bernoulli filtering’, IEEE J. Select. Top. Signal Process., 2013, 7, (3), pp. 399–409.
    23. 23)
      • 7. Hoseinnezhad, R., Vo, B.-N., Vo, B.-T.: ‘Visual tracking in background subtracted image sequences via multi-Bernoulli filtering’, IEEE Trans. Signal Process., 2013, 61, (2), pp. 392–397.
    24. 24)
      • 20. Vo, B.-T., Vo, B.-N., Phung, D.: ‘Labeled random finite sets and the Bayes multi-target tracking filter’, IEEE Trans. Signal Process., 2014, 62, (24), pp. 6554–6567.
    25. 25)
      • 14. Vo, B.-T., Vo, B.-N., Cantoni, A.: ‘Analytic implementations of the cardinalized probability hypothesis density filter’, IEEE Trans. Signal Process., 2007, 55, (7), pp. 3553–3567.
    26. 26)
      • 27. Yoon, J.H., Lee, C.-R., Yang, M.-H., et al: ‘Online multi-object tracking via structural constraint event aggregation’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, Nevada, USA, 2016.
    27. 27)
      • 16. Kim, D.Y., Jeon, M.: ‘Robust multi-Bernoulli filtering for visual tracking’. Proc. IEEE Conf. Control Automation and Information Sciences, Gwangju, South Korea, 2014.
    28. 28)
      • 3. Mahler, R., Vo, B.-T., Vo, B.-N.: ‘CPHD filtering with unknown clutter rate and detection profile’, IEEE Trans. Signal Process., 2011, 59, (8), pp. 3497–3513.
    29. 29)
      • 32. Yuan, Y., Lu, Y., Wang, Q.: ‘Tracking as a whole: multi-target tracking by modeling group behavior with sequential detection’, IEEE Trans. Intell. Transp. Syst., 2017, 18, (2), pp. 3339–3349.
    30. 30)
      • 22. Yoon, J.H., Yang, M.-H., Lim, J., et al: ‘Bayesian multi-object tracking using motion context from multiple objects’. Proc. IEEE Winter Conf. on Applications of Computer Vision (WACV), Hawaii, USA, 2015.
    31. 31)
      • 1. Mahler, R.: ‘Statistical multisource-multitarget information Fusion’ (Artech House, Norwood, MA, 2007).
    32. 32)
      • 8. Zhang, L., Li, Y., Nevatia, R.: ‘Global data association for multi-object tracking using network flows’. Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, USA, 2008.
    33. 33)
      • 23. Kuo, C.-H., Nevatia, R.: ‘How does person identity recognition help multi-person tracking?’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Colorado Springs, Colorado, USA, 2011.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2017.0600
Loading

Related content

content/journals/10.1049/iet-cvi.2017.0600
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading