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2015 | 5 | 875--881
Tytuł artykułu

Pedestrian Tracking in Video Sequences: a Particle Filtering Approach

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this work we study the methods for pedestrian tracking in video sequences and indicate various applications of these methods ranging from surveillance systems to aiding the visually impaired persons. First, we define the general problem of object tracking that comprises the tasks of object detection, identifying the flow of object location in consecutive video images and finally analysis of the tracked trajectory data. We review the well known object tracking techniques i.e. the Mean-Shift and the CAMSHIFT algorithm and discuss their properties. Then we introduce the computational technique known as particle filtering (PF) and explain how we have applied it to the tasks of pedestrian tracking. We compare the PF approach against the Mean-Shift and the CAMSHIFT algorithms in terms of tracking robustness and the required computational demand. We conclude, that on the tested video sequences, the PF tracker outperforms the Mean- Shift and by a small margin the CAMSHIFT algorithm. The PF tracker requires more computational power, however, its tracking performance can be flexibly adjusted to the application requirements.(original abstract)
Słowa kluczowe
PL
EN
Rocznik
Tom
5
Strony
875--881
Opis fizyczny
Twórcy
  • Lodz University of Technology
  • Lodz University of Technology
  • Lodz University of Technology
Bibliografia
  • A. Yilmaz, O. Javed, and M. Shah, "Object tracking: A survey," ACM Computing Surveys, vol. 38, no. 4, dec 2006. doi: http://dx.doi.org/10.1145/1177352.1177355
  • J. Kulchandani and K. Dangarwala, "Moving object detection: Review of recent research trends," in Pervasive Computing (ICPC), 2015 International Conference on, Jan 2015. doi: 10.1109/PERVASIVE. 2015.7087138 pp. 1-5.
  • Y. Wu, J. Lim, and M. Yang, "Online object tracking: A benchmark," in 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR, USA: IEEE, June 2013. doi: 10.1109/CVPR.2013.312 pp. 2411-2418. [Online]. Available: http://dx.doi.org/10.1109/CVPR. 2013.312
  • P. Baranski and P. Strumillo, "Enhancing positioning accuracy in urban terrain by fusing data from a GPS receiver, inertial sensors, stereocamera and digital maps for pedestrian navigation," Sensors, vol. 12, no. 6, pp. 6764-6801, 2012. doi: http://dx.doi.org/10.3390/s120606764
  • M. Bujacz, P. Skulimowski, and P. Strumillo, "Naviton-a prototype mobility aid for auditory presentation of three-dimensional scenes to the visually impaired," Journal of the Audio Engineering Society, vol. 60, no. 9, pp. 696-708, 2012.
  • K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 27, no. 10, pp. 1615-1630, Oct 2005. doi: http://dx.doi.org/10.1109/cvpr.2003.1211478
  • D. Comaniciu, V. Ramesh, and P. Meer, "Real-time tracking of non-rigid objects using mean shift," Computer Vision and Pattern Recognition, vol. 2, no. 1, pp. 142-149, 2000. doi: http://dx.doi.org/10.1109/cvpr.2000.854761
  • D. Comaniciu and V. Ramesh, "Mean shift and optimal prediction for efficient object tracking," in Image Processing, 2000. Pro- ceedings. 2000 International Conference on, vol. 3, 2000. doi: http://dx.doi.org/10.1109/icip.2000.899297. ISSN 1522-4880 pp. 70-73.
  • D. Forsyth and J. Ponce, Computer Vision: A Modern Approach. Prentice Hall Professional Technical Reference, 2002. ISBN 0130851981
  • P. Viola, M. J. Jones, and D. Snow, "Detecting pedestrians using patterns of motion and appearance," in Computer Vision, 2003. Pro- ceedings. Ninth IEEE International Conference on, vol. 2, Oct 2003. doi: http://dx.doi.org/10.1109/iccv.2003.1238422 pp. 734-741.
  • B. Deori and D. M. Thounaojam, "A survey on moving object tracking in video," International Journal on Information Theory, vol. 3, no. 3, July 2014. doi: http://dx.doi.org/10.5121/ijit.2014.3304
  • A. Materka and M. Strzelecki, "Texture analysis methods-a review," Technical University of Lodz, Institute of Electronics, Brussels, Tech. Rep., 1998, cOST B11 report.
  • A. Yilmaz, K. Shafique, N. Lobo, X. Li, T. Olson, and M. A. Shah, "Target-tracking in flir imagery using mean-shift and global motion compensation," in Workshop on Computer Vision Beyond the Visible Spectrum, Kauai, 2001, pp. 54-58.
  • G. R. Bradski, "Real time face and object tracking as a component of a perceptual user interface," in Proceedings of the Fourth IEEE Workshop on Applications of Computer Vision, 1998. doi: http://dx.doi.org/10.1109/acv.1998.732882
  • I. Michael and B. Andrew, "Condensation - conditional density propagation for visual tracking," International Journal of Computer Vision, vol. 29, pp. 5-28, 1998.
  • N. Gordon and D. Salmond, "Bayesian state estimation for tracking and guidance using the bootstrap filter," Journal of Guidance, Control and Dynamics, vol. 18, no. 6, pp. 1434-1443, 1995. doi: http://dx.doi.org/10.2514/6.1993-3701
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171424700

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