Abstract
Multitarget tracking is an important topic in visual surveillance system. Considering imperfections of the cardinalized probability hypothesis density (CPHD) filter and the target maneuvers, we propose an adaptive genetic multiple-model CPHD filter in this paper. First, we discuss the filtering process and combined the standard CPHD filter with the multiple-model-based framework. Afterward, the sequential Monte Carlo implementation of the proposed filter for the nonlinear and non-Gaussian state estimates is presented in detail. To enhance the tracking performance as target start to maneuver, the adaptive genetic algorithm is used to improve the target state estimation accuracy at the time of state switching with the excellent particles. On the other hand, the undetected component of the measurement-updated weight of survival particle is compensated by the excess weight of newborn particle to correct the number estimates of targets. The simulation results are provided to illustrate the reliability and efficiency of the proposed filter.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00500-016-2087-0/MediaObjects/500_2016_2087_Fig1_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00500-016-2087-0/MediaObjects/500_2016_2087_Fig2_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00500-016-2087-0/MediaObjects/500_2016_2087_Fig3_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00500-016-2087-0/MediaObjects/500_2016_2087_Fig4_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00500-016-2087-0/MediaObjects/500_2016_2087_Fig5_HTML.gif)
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Blackman SS (2004) Multiple hypothesis tracking for multiple target tracking. IEEE Aerosp Electron Syst Mag 19(1):5–18
Chen X, Tharmarasa R, McDonald M, Kirubarajan T (2011) A multiple model cardinalized probability hypothesis density filter. MaMaster University, Cananda
Dai CH, Zhu YF, Chen WR (2006) Adaptive probabilities of crossover and mutation in genetic algorithms based on cloud model. Proceedings of the IEEE information theory workshop, Chengdu
Daniel M, Stephan R, Benjamin W, Klaus D (2013) Road user tracking using a Dempster–Shafer based classifying multiple-model PHD filter. In: Proceedings of the 16th international conference on information fusion, Istanbul, pp 1236–1242
Georgescu R, Willet P (2011) Multiple model cardinalized probability hypothesis density filter. In: Proceedings of the SPIE signal and data processing of small targets, San Diego, pp 81370L
Georgescu R, Willet P (2012) The multiple model CPHD tracker. IEEE Trans Signal Process 60(4):1741–1751
Gning A, Julier SJ, Barr J, Anderson J, Mill D, Williams ML (2014) PHD filter in presence of highly structured sea clutter process and tracks with extent. In: Proceedings of the IET conference on data fusion and target tracking: algorithms and applications, Liverpool, pp 1–8
Jing ZL (2005) Neural network-based state fusion and adaptive tracking for maneuvering targets. Commun Nonlinear Sci Numer Simul 10:340–395
Li WL, Jia YM, Du JP, Yu FS (2011) Gaussian mixture PHD smoother for jump Markov models in multiple maneuvering targets tracking. In: Proceedings of the American control conference, San Francisco, pp 3025–3029
Lin Y, Barshalom Y, Kirubarajan T (2006) Track labeling and PHD filter for multitarget tracking. IEEE Trans Aerosp Electron Syst 42(3):778–795
Liu Z, Xu SQ, Zhang Y, Chen X, Chen CLP (2014) Interval type-2 fuzzy kernel based support vector machine algorithm for scene classification of humanoid robot. Soft Comput 18(3):589–606
Ma Q, Huang WJ, Wang LQ, Yang SY (2009) Genetic algorithm optimized unscented particle filter method. J Tianjin Univ Technol 23(5):46–49
Ma C, San Y, Zhu Y (2013) Multiple model truncated particle filter for maneuvering target tracking. In: Proceedings of the 32th Chinese control conference, Xi’an, pp 4773–4777
Mahelr R (2007) Unified sensor management using CPHD filters. In: Proceedings of the 10th conference on information fusion, Quebec, pp 1–7
Mahelr R (2007) Statistical multisource-multitarget information fusion. Artech House, Norwood
Mahler R (2009) PHD filters for nonstandard target. In: Proceedings of the 12th conference on information fusion, Seattle, pp 915–921
Mahler R, Vo BT, Vo BN (2011) CPHD filtering with unknown clutter rate and detection profile. IEEE Trans Signal Process 59(8):3497–3513
Musicki D, Evans R (2004) Joint integrated probabilistic data association—JIPDA. IEEE Trans Aerosp Electron Syst 40(3):1093–1099
Nadarajan N, Kirubarajan T, Lang T, Mcdonald M, Punithakumar K (2011) Multitarget tracking using probability hypothesis density smoothing. IEEE Trans Aerosp Electron Syst 47(4):2344–2360
Ouyang C, Ji HB (2012) Improved Gaussian mixture CPHD tracker for multitarget tracking. IEEE Trans Aerosp Electron Syst 49(2):1177–1191
Ouyang C, Ji HB, Guo ZQ (2012) Improved multiple model particle PHD and CPHD filters. Acta Autom Sin 38(3):341–348
Panta K (2007) Multi-target tracking using 1st moment of random finite sets. The University of Melbourne, Melbourne
Panta K, Clark DE, Vo BN (2009) Data association and track management for the Gaussian mixture probability hypothesis density filter. IEEE Trans Aerosp Electron Syst 45(3):1003–1016
Panta K, Clark DE, Vo BN (2009) Data association and track management for the Gaussian mixture probability hypothesis density filter. IEEE Trans Aerosp Electron Syst 45(3):1003–1016
Pasha A, Vo BN, Tuan HD, Ma WK (2009) A Gaussian mixture PHD filter for jump Markov systems models. IEEE Trans Aerosp Electron Syst 46(3):919–936
Punithakumar K, Kirubarajan T, Sinha A (2008) Multiple-model probability hypothesis density filter for tracking maneuvering targets. IEEE Trans Aerosp Electron Syst 44(1):87–98
Srinivas M, Patnaik LM (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern 24(4):656–667
Ulmke M, Erdinc O, Willett P (2007) Gaussian mixture Cardinalized PHD filter for ground moving target tracking. In: Proceedings of the 10th conference on information fusion, Quebec, pp 1–8
Ulmke M, Franken D, Schmidt M (2008) Missed detection problems in the cardinalized probability hypothesis density filter. In: Proceedings of the 11th conference on information fusion, Cologne, pp 1–7
Ulmke M, Erdinc O, Willett P (2010) GMTI tracking via the Gaussian mixture cardinalized probability hypothesis density filter. IEEE Trans Aerosp Electron Syst 46(4):1821–1833
Vo BT (2008) Random finite sets in multi-objective filtering. The University of Western Australia, Perth
Wang RG, Li MM, Wu M, Shen FL (2011) A new particle filter algorithm based on the adaptive generic algorithm. J Univ Sci Technol China 41(1):134–141
Wen GX, Liu YJ, Tong SC, Li XL (2011) Adaptive neural output feedback control of nonlinear discrete-time systems. Nonlinear Dyn 65(1–2):65–75
Xu BL, Wang ZQ (2007) A multi-objective-ACO-based data association method for bearings-only multi-target tracking. Commun Nonlinear Sci Numer Simul 10:1360–1369
Yang JL, Ji HB (2012) A novel track maintenance algorithm for PHD/CPHD filter. Signal Process 92:2371–2380
Yu GS, Yu XW (2015) An improved adaptive genetic algorithm. Math Pract Theory 45(19):259–264
Zhang J, Chung H, Lo WL (2007) Clustering-based adaptive crossover and mutation probabilities for genetic algorithms. IEEE Trans Evolut Comput 11(3):326–335
Zhang SJ, Yang H, Zeng K, Zhang H, Wang YC (2010) Particle filter tracking algorithm based on genetic algorithm. Opto Electron Eng 37(10):16–22
Zhou WH, Zhang HB, Ji YR (2012) Multi-target tracking algorithm based on SMC-CPHD filter. J Astronaut 33(4):443–450
Acknowledgments
The work was supported by the Foundation of Education Department of Liaoning Province (L2015230), the Doctoral Scientific Research Foundation of Liaoning Province (201601149), and the National Natural Science Foundation of China (61473139, 61503169). The authors would like to thank the anonymous reviewers for their helpful comments and advices which contributed much to the improvements of this paper.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Communicated by V. Loia.
Rights and permissions
About this article
Cite this article
Li, B., Zhao, J. & Pang, F. Adaptive genetic MM-CPHD filter for multitarget tracking. Soft Comput 21, 4755–4767 (2017). https://doi.org/10.1007/s00500-016-2087-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-016-2087-0