Bayesian Multiple Person Tracking using Probability Hypothesis Density Smoothing

[1]R. Mahler, Multitarget Bayes filtering via first-order multitarget moments, IEEE Transactions on Aerospace and Electronic Systems 39 (4) (2003) 1152–1178.10.1109/TAES.2003.1261119 Search in Google Scholar

[2]R. Mahler, Statistical Multisource-Multitarget Information Fusion, Artech House, Norwood, 2007.10.1201/9781420053098.ch16 Search in Google Scholar

[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 0932-8092.10.1007/s00138-008-0132-4 Search in Google Scholar

[4]D. A. Forsyth, J. Ponce, Computer Vision: A Modern Approach, Prentice Hall Professional Technical Reference, ISBN 0130851981, 2002. Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar

[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, 2007.10.1117/12.734656 Search in Google Scholar

[8]S. Hernandez, Smoothing algorithms for the Probability Hypothesis Density filter (ECSTR10-13), Tech. Rep., Victoria University of Wellington, 2010. Search in Google Scholar

[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. Search in Google Scholar

[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 0162-8828.10.1109/TPAMI.2007.7077018550903 Search in Google Scholar

[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. Search in Google Scholar

[12]D. Ramanan, D. A. Forsyth, A. Zisserman, Tracking people by learning their appearance, IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (2007) 65–81.10.1109/TPAMI.2007.250600 Search in Google Scholar

[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 0162-8828.10.1109/34.868677 Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar

[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., 2007.10.1109/ICASSP.2007.366104 Search in Google Scholar

[20]N. Pham, W. Huang, S. Ong, Probability Hypothesis Density Approach for Multi-camera Multi-object Tracking, Computer Vision –ACCV 2007 (2007) 875– 884. Search in Google Scholar

[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 Technology, IEEE Transactions on 18 (8) (2008) 1085–1095, ISSN 1051-8215.10.1109/TCSVT.2008.927105 Search in Google Scholar

[22]E. Maggio, A. Cavallaro, Learning Scene Context for Multiple Object Tracking, Image Processing, IEEE Transactions on 18 (8) (2009) 1873–1884, ISSN 1057- 7149.10.1109/TIP.2009.201993419423445 Search in Google Scholar

[23]R. L. Streit, Poisson Point Processes: Imaging, Tracking, and Sensing, Springer, 2010.10.1007/978-1-4419-6923-1 Search in Google Scholar

[24]J. F. C. Kingman, Poisson Processes, Oxford University Press, 1993. Search in Google Scholar

[25]C. A. B, N. Vasconcelos, Bayesian Poisson regression for crowd counting, in: EEE 12th International Conference on Computer Vision, 2009. Search in Google Scholar

[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, 2005.10.1117/12.618730 Search in Google Scholar

[27]S. Hernandez, P. Teal, Multi-target Tracking with Poisson Processes Observations, in: Advances in Image and Video Technology, 2007. Search in Google Scholar

[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) 1224–1245.10.1109/TAES.2005.1561884 Search in Google Scholar

[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 0018-9251.10.1109/TAES.2007.4441750 Search in Google Scholar

[30]C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer-Verlag New York, Inc., Secaucus, NJ, USA, ISBN 0387310738, 2006. Search in Google Scholar

[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 1751-8784.10.1049/iet-rsn:20070051 Search in Google Scholar

[32]W. R. Gilks, S. Richardson, Markov Chain Monte Carlo in Practice, Chapman Hall, New York, 1996.10.1201/b14835 Search in Google Scholar

[33]C. P. Robert, G. Casella, Monte Carlo Statistical Methods (Springer Texts in Statistics), Springer-Verlag New York, Inc., Secaucus, NJ, USA, ISBN 0387212396, 2005.10.1007/978-1-4757-4145-2 Search in Google Scholar

[34]O. Erdinc, P. Willett, Y. Bar-Shalom, The Bin-Occupancy Filter and its Connection to the PHD Filters, Signal Processing, IEEE Transactions on . Search in Google Scholar

[35]M. Briers, A. Doucet, S. Maskell, Smoothing algorithms for state–space models, Annals of the Institute of Statistical Mathematics 62 (1) (2010) 61–89.10.1007/s10463-009-0236-2 Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar

[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.10.1109/TSP.2008.920469 Search in Google Scholar

[40]Y. Bar-Shalom, X. R. Li, T. Kirubarajan, Estimation with applications to tracking and navigation, Wiley Interscience, New York, 2001.10.1002/0471221279 Search in Google Scholar

[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. Search in Google Scholar