Visual tracking of numerous targets via multi-Bernoulli filtering of image data

Pattern Recognition

Volumn 45, Issue 10, 2012, Pages 3625-3635

  Hoseinnezhad, Reza ^a   Vo, Ba Ngu ^b   Vo, Ba Tuong ^b   Suter, David ^c  

^a RMIT UNIVERSITY   (Australia)

^b UNIVERSITY OF WESTERN AUSTRALIA   (Australia)

^c UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

Multi target tracking; Random finite sets; Track before detect; Visual tracking

Indexed keywords

BAYESIAN METHODS; CELL TRACKING; DETECTION ERROR; FALSE ALARMS; FALSE NEGATIVES; FALSE POSITIVE; FINITE SET REPRESENTATION; IMAGE DATA; IMAGE SEQUENCE; LABEL SWITCHING; MULTI-TARGET TRACKING; MULTITARGET; RANDOM FINITE SETS; TRACK BEFORE DETECT; TRACK MULTIPLE TARGETS; TRACKING ERRORS; VISUAL TRACKING;

BAYESIAN NETWORKS;

TRACKING (POSITION);

EID: 84861793833     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2012.04.004     Document Type: Article

References (39)

1. Q. Yu, and G. Medioni Multiple-target tracking by spatiotemporal Monte Carlo Markov chain data association IEEE Transactions on Pattern Analysis and Machine Intelligence 31 12 2009 2196 2210
2. M. Kristan, J. Per, M. Pere, and S. Kovacic Closed-world tracking of multiple interacting targets for indoor-sports applications Computer Vision and Image Understanding 113 5 2009 598 611
3. A. Elgammal, R. Duraiswami, D. Harwood, and L.S. Davis Background and foreground modeling using nonparametric kernel density estimation for visual surveillance Proceedings of the IEEE 90 7 2002 1151 1162
4. M. Yang, T. Yu, Y. Wu, Game-theoretic multiple target tracking, in: ICCV07, Rio de Janeiro, Brazil, 2007.
5. H. Wang, and D. Suter A consensus-based method for tracking: modelling background scenario and foreground appearance Pattern Recognition 40 3 2007 1091 1105
6. T. Zhao, R. Nevatia, and B. Wu Segmentation and tracking of multiple humans in crowded environments IEEE Transactions on Pattern Analysis and Machine Intelligence 30 7 2008 1198 1211
7. S. Apewokin, B. Valentine, R. Bales, L. Wills, S. Wills, Tracking multiple pedestrians in real-time using kinematics, in: CVPR08 Workshops, Anchorage, AK, United States, 2008.
8. L. Zhu, J. Zhou, and J. Song Tracking multiple objects through occlusion with online sampling and position estimation Pattern Recognition 41 8 2008 2447 2460
9. R. Abbott, and L. Williams Multiple target tracking with lazy background subtraction and connected components analysis Machine Vision and Applications 20 2 2009 93 101
10. Y. Bar-Shalom, T. Fortmann, M. Scheffe, Joint probabilistic data association for multiple targets in clutter, in: Proceedings of the Conference on Information Sciences and Systems.
11. D. Reid An algorithm for tracking multiple targets IEEE Transactions on Automatic Control 24 6 1979 843 854
12. I. Cox, and S. Hingorani An efficient implementation of Reids multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking IEEE Transactions on Pattern Analysis and Machine Intelligence 18 2 1996 138 150
13. K.-C. Chang, S. Mori, and C.-Y. Chong Evaluating a multiple-hypothesis multitarget tracking algorithm IEEE Transactions on Aerospace and Electronic Systems 20 2 1994 578 590
14. S.-W. Joo, and R. Chellappa A multiple-hypothesis approach for multiobject visual tracking IEEE Transactions on Image Processing 16 11 2007 2849 2854
15. B.-N. Vo, B.-T. Vo, N.-T. Pham, and D. Suter Joint detection and estimation of multiple objects from image observations IEEE Transactions on Signal Processing 58 10 2010 5129 5141
16. S. Buzzi, M. Lops, L. Venturino, and M. Ferri Track-before-detect procedures in a multi-target environment IEEE Transactions on Aerospace and Electronic Systems 44 3 2008/07/ 1135 1150
17. M. Isard, J. MacCormick, BraMBLe: a Bayesian multiple-blob tracker, in: ICCV01, vol. 2, Vancouver, British Columbia, Canada, 2001, pp. 34-41.
18. P. Perez, C. Hue, J. Vermaak, M. Gangnet, Color-based probabilistic tracking, in: ECCV, Copenhagen, Denmark, 2002, pp. 661-675.
19. K. Nummiaro, E. Koller-Meier, L. Van Gool, Object tracking with an adaptive color-based particle filter, in: DAGM, Zurich, Switzerland, 2002, pp. 353-360.
20. J. Vermaak, S. Maskell, M. Briers, P. Perez, Bayesian visual tracking with existence process, in: ICIP, Atlanta, GA, USA, 2006, pp. 721-724.
21. K. Okuma, A. Taleghani, N. De Freitas, J. Little, D. Lowe, A boosted particle filter: multitarget detection and tracking, in: ECCV04, vol. 3021, 2004, pp. 28-39.
22. J. Vermaak, A. Doucet, P. Perez, Maintaining multi-modality through mixture tracking, vol. 2, Nice, France, 2003, pp. 1110-1116.
23. J. Czyz, B. Ristic, and B. Macq A particle filter for joint detection and tracking of color objects Image and Vision Computing 25 8 2007 1271 1281
24. D. Schuhmacher, B.-T. Vo, and B.-N. Vo A consistent metric for performance evaluation in multi-object filtering IEEE Transactions on Signal Processing 56 8 2008 3447 3457
25. R. Mahler, Statistical multisource-multitarget information fusion, Artech House, Norwood, MA, USA, 2007.
26. P. Moral, Feynman-Kac formulae: genealogical and interacting particle systems with applications, in: Springer Series in Statistics: Probability and its Applications, Springer, 2004.
27. K. Smith, D. Gatica-Perez, J. Odobez, Using particles to track varying numbers of objects, in: CVPR, San Diego, USA, 2005.
28. K. Smith, S. Ba, D. Gatica-Perez, J. Odobez, Tracking the multi-person wandering visual focus of attention, in: International Conference on Multimodal Interfaces (ICMI), Banff, Canada, 2006.
29. Z. Khan, T. Balch, and F. Dellaert MCMC-based particle filtering for tracking a variable number of interacting targets IEEE Transactions on Pattern Analysis and Machine Intelligence 27 11 2005 1805 1918
30. T. Zhao, R. Nevatia, Tracking multiple humans in crowded environment, in: CVPR, vol. II, Washington D.C., USA, 2004, pp. 406-413.
31. B.-T. Vo, B.-N. Vo, and A. Cantoni Analytic implementations of the cardinalized probability hypothesis density filter IEEE Transactions on Signal Processing 55 7 2007 3553 3567
32. B.-T. Vo, B.-N. Vo, and A. Cantoni The cardinality balanced multi-target multi-Bernoulli filter and its implementations IEEE Transactions on Signal Processing 57 2 2009 409 423
33. N.T. Pham, H. Weimin, S. Ong, Tracking multiple objects using probability hypothesis density filter and color measurements, in: IEEE International Conference on Multimedia and Expo, ICME07, Beijing, China, 2007, pp. 1511-1514.
34. Y.-D. Wang, J.-K. Wu, W. Huang, A.A. Kassim, Gaussian mixture probability hypothesis density for visual people tracking, in: International Conference on Information Fusion, Quebec City, Canada, 2007.
35. K. Shafique, and M. Shah A noniterative greedy algorithm for multiframe point correspondence IEEE Transactions on Pattern Analysis and Machine Intelligence 27 1 2005 51 65
36. R.P. Mahler Multitarget Bayes filtering via first-order multitarget moments IEEE Transactions on Aerospace and Electronic Systems 39 4 2003 1152 1178
37. B.-N. Vo, S. Singh, and A. Doucet Sequential Monte Carlo methods for multi-target filtering with random finite sets IEEE Transactions on Aerospace and Electronic Systems 41 4 2005 1224 1245
38. S.S. Blackman, R. Popoli, Design and Analysis of Modern Tracking Systems, Artech House, 1999.
39. H. Medeiros, J. Park, A. Kak, A parallel color-based particle filter for object tracking, in: CVPR, Anchorage, Alaska, USA, 2008, pp. 1-8.