BAYESIAN MULTIPLE PERSON TRACKING USING PROBABILITY HYPOTHESIS DENSITY SMOOTHING

Publications

Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

GET ALERTS

eISSN: 1178-5608

DESCRIPTION

2
Reader(s)
4
Visit(s)
0
Comment(s)
0
Share(s)

VOLUME 4 , ISSUE 2 (June 2011) > List of articles

BAYESIAN MULTIPLE PERSON TRACKING USING PROBABILITY HYPOTHESIS DENSITY SMOOTHING

S. Hernandez * / M. Frean *

Keywords : Power system, fault current, current limiter, permanent magnet, saturable core, magnetic current limiter, high temperature superconducting fault current limiter.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 4, Issue 2, Pages 285-312, DOI: https://doi.org/10.21307/ijssis-2017-440

License : (CC BY-NC-ND 4.0)

Received Date : 03-May-2011 / Accepted: 24-May-2011 / Published Online: 01-June-2011

ARTICLE

ABSTRACT

We presents a PHD filtering approach to estimate the state of an unknown number of persons in a video sequence. Persons are represented by moving blobs, which are tracked across different frames using a first-order moment approximation to the posterior density. The PHD filter is a good alternative to standard multi-target tracking algorithms, since overrides making explicit associations between measurements and persons locations. The recursive method has linear complexity in the number of targets, so it also has the potential benefit of scaling well with a large number of persons being tracked. The PHD filter achieves interesting results for the multiple persons tracking problem, albeit discarding useful information from higher order interactions. Nevertheless, a backward state-space representation using PHD smoothing can be used to refine the filtered estimates. In this paper, we present two smoothing strategies for improving PHD filter estimates in multiple persons tracking. Results from using PHD smoothing techniques in a video sequence shows a slight gain in the cardinality estimates (meaning the number of persons in a particular video frame), but good performance in the individual location estimates.

Content not available PDF Share

FIGURES & TABLES

REFERENCES

[1] R. Mahler, Multitarget Bayes filtering via first-order multitarget moments, IEEE Transactions on Aerospace and Electronic Systems 39 (4) (2003) 1152–1178.
[2] R. Mahler, Statistical Multisource-Multitarget Information Fusion, Artech House,Norwood, 2007.
[3] B. Zhan, D. N. Monekosso, P. Remagnino, S. A. Velastin, L. Q. Xu, Crowd analysis: a survey, Mach. Vision Appl. 19 (5-6) (2008) 345–357, ISSN 0932-8092.
[4] D. A. Forsyth, J. Ponce, Computer Vision: A Modern Approach, Prentice Hall
Professional Technical Reference, ISBN 0130851981, 2002.
[5] T. Zhao, R. Nevatia, Bayesian human segmentation in crowded situations, in:Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on, vol. 2, ISSN 1063-6919, II–459–66 vol.2.
[6] M. Isard, A. Blake, A Smoothing Filter for CONDENSATION, in: Proceedings of the 5th European Conference on Computer Vision, vol. 1406, Springer-Verlag,767–781, 1998.
[7] N. Nandakumaran, K. Punithakumar, T. Kirubarajan, Improved multi-target tracking using probability hypothesis density smoothing, in: Signal and Data
Processing of Small Targets 2007, URL http://link.aip.org/link/?PSI/6699/66990M/1, 2007.
[8] S. Hernandez, Smoothing algorithms for the Probability Hypothesis Density filter (ECSTR10-13), Tech. Rep., Victoria University of Wellington, 2010.
[9] B.-N. Vo, B.-T. Vo, N. T. Pham, D. Suter, Bayesian multi-object estimation from image observations, in: Information Fusion, 2009. FUSION ’09. 12th International Conference on, 890–898, 2009.
[10] T. Zhao, R. Nevatia, B. Wu, Segmentation and Tracking of Multiple Humans in Crowded Environments, Pattern Analysis and Machine Intelligence, IEEE Transactions on 30 (7) (2008) 1198–1211, ISSN 0162-8828.
[11] S. Khan, M. Shah, Tracking multiple occluding people by localizing on multiple scene planes, IEEE Transactions of Pattern Analysis and Machine Intelligence 3(2008) 505–519.
[12] D. Ramanan, D. A. Forsyth, A. Zisserman, Tracking people by learning their appearance, IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (2007) 65–81.
[13] C. Stauffer, W. Grimson, Learning patterns of activity using real-time tracking,Pattern Analysis and Machine Intelligence, IEEE Transactions on 22 (8) (2000)747–757, ISSN 0162-8828.
[14] M. Isard, J. MacCormick, BraMBLe: a Bayesian multiple-blob tracker, Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on 2 (2001) 34–41.
[15] N. Dalal, B. Triggs, Histograms of Oriented Gradients for Human Detection,in: CVPR ’05: Proceedings of the 2005 IEEE Computer Society Conference
on Computer Vision and Pattern Recognition (CVPR’05) - Volume 1, IEEE Computer Society, Washington, DC, USA, ISBN 0-7695-2372-2, 886–893, 2005.
[16] N. Ikoma, T. Uchino, H. Maeda, Tracking of feature points in image sequence by SMC implementation of PHD filter, in: SICE 2004 Annual Conference, 2004.
[17] Y. Wang, J. Wu, A. Kassim, W. Huang, Tracking a Variable Number of Human Groups in Video Using Probability Hypothesis Density, in: ICPR 2006. 18th
International Conference on Pattern Recognition, 2006.
[18] Y.-D. Wang, J.-K. Wu, W. Huang, A. Kassim, Gaussian mixture probability hypothesis density for visual people racking, in: Information Fusion, 2007 10th International Conference on, 1–6, Dec. 2007.
[19] E. Maggio, E. Piccardo, C. Regazzoni, A. Cavallaro, Particle PHD Filtering for Multi-Target Visual Tracking, in: IEEE International Conference on Acoustics,Speech and Signal Processing, 2007. ICASSP 2007., 2007.
[20] N. Pham, W. Huang, S. Ong, Probability Hypothesis Density Approach for Multi-camera Multi-object Tracking, Computer Vision –ACCV 2007 (2007) 875–884.
[21] Y.-D. Wang, J.-K. Wu, A. Kassim, W. Huang, Data-Driven Probability Hypothesis Density Filter for Visual Tracking, Circuits and Systems for Video Tech-
nology, IEEE Transactions on 18 (8) (2008) 1085–1095, ISSN 1051-8215.
[22] E. Maggio, A. Cavallaro, Learning Scene Context for Multiple Object Tracking,
Image Processing, IEEE Transactions on 18 (8) (2009) 1873–1884, ISSN 1057-7149.
[23] R. L. Streit, Poisson Point Processes: Imaging, Tracking, and Sensing, Springer,2010.
[24] J. F. C. Kingman, Poisson Processes, Oxford University Press, 1993.
[25] C. A. B, N. Vasconcelos, Bayesian Poisson regression for crowd counting, in: EEE 12th International Conference on Computer Vision, 2009.
[26] K. Gilholm, S. Godsill, S. Maskell, D. Salmond, Poisson models for extended target and group tracking, in: Signal and Data Processing of Small Targets 2005,SPIE, 2005.
[27] S. Hernandez, P. Teal, Multi-target Tracking with Poisson Processes Observations, in: Advances in Image and Video Technology, 2007.
[28] B. Vo, S. Singh, A. Doucet, Sequential Monte Carlo methods for multitarget filtering with random finite sets, IEEE Transactions on Aerospace and Electronic Systems 41 (4) (2005) 1224–1245.
[29] D. Clark, J. Bell, Multi-target state estimation and track continuity for the particle PHD filter, Aerospace and Electronic Systems, IEEE Transactions on
43 (4) (2007) 1441–1453, ISSN 0018-9251.
[30] C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer-Verlag New York, Inc., Secaucus, NJ, USA, ISBN
0387310738, 2006.
[31] M. Tobias, A. Lanterman, Techniques for birth-particle placement in the probability hypothesis density particle filter applied to passive radar, Radar, Sonar & Navigation, IET 2 (5) (2008) 351–365, ISSN 1751-8784.
[32] W. R. Gilks, S. Richardson, Markov Chain Monte Carlo in Practice, Chapman Hall, New York, 1996.
[33] C. P. Robert, G. Casella, Monte Carlo Statistical Methods (Springer Texts in Statistics), Springer-Verlag New York, Inc., Secaucus, NJ, USA, ISBN
0387212396, 2005.
[34] O. Erdinc, P. Willett, Y. Bar-Shalom, The Bin-Occupancy Filter and its Connection to the PHD Filters, Signal Processing, IEEE Transactions on .
[35] M. Briers, A. Doucet, S. Maskell, Smoothing algorithms for state–space models,Annals of the Institute of Statistical Mathematics 62 (1) (2010) 61–89.
[36] O. E. Drummond, B. E. Fridling, Ambiguities in evaluating performance of multiple target tracking algorithms, in: O. E. Drummond (Ed.), Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 1698 of Presented at the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference, 326–337, 1992.
[37] J. Hoffman, R. Mahler, Multitarget miss distance via optimal assignment, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 34 (3) (2004) 327–336.
[38] B. T. Vo, Random Finite Sets in Multi-Object Filtering, Ph.D. thesis, School of Electrical, Electronic and Computer Engineering. The University of Western Australia, 2009.
[39] D. Schuhmacher, B.-T. Vo, B.-N. Vo, A Consistent Metric for Performance Evaluation of Multi-Object Filters, Signal Processing, IEEE Transactions on
56 (8) (2008) 3447–3457, ISSN 1053-587X.
[40] Y. Bar-Shalom, X. R. Li, T. Kirubarajan, Estimation with applications to tracking and navigation, Wiley Interscience, New York, 2001.
[41] M. Ulmke, D. Franken, M. Schmidt, Missed detection problems in the cardi-nalized probability hypothesis density filter, in: Information Fusion, 2008 11th
International Conference on, 2008.

EXTRA FILES

COMMENTS