Improved Method Based on MPF for Multi-target Tracking
Particle filter algorithm is poor at consistently maintaining the multi-modality problem and remains particle degeneracy phenomenon. This shortcoming can be addressed through using Mixture Particle Filter (MPF) to model the target distribution as a non-parametric mixture model. According to computat...
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| Published in | AASRI procedia Vol. 3; pp. 177 - 183 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
2012
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2212-6716 2212-6724 2212-6724 |
| DOI | 10.1016/j.aasri.2012.11.030 |
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| Abstract | Particle filter algorithm is poor at consistently maintaining the multi-modality problem and remains particle degeneracy phenomenon. This shortcoming can be addressed through using Mixture Particle Filter (MPF) to model the target distribution as a non-parametric mixture model. According to computational complexity of the particle filter algorithm, Gaussian Particle Filter (GPF) is used. The particle set is obtained by sampling of Gaussian density function instead of re-sampling to improve the computing speed of particle filter algorithm. In this paper, combined with the advantages of GPF and MPF, GM-MPF algorithm is proposed to improve the real-time of MPF. The experimental results show that GM-MPF algorithm can effectively solve the problem of multi-modality, particle degradation and achieve real-time multi-target tracking. |
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| AbstractList | Particle filter algorithm is poor at consistently maintaining the multi-modality problem and remains particle degeneracy phenomenon. This shortcoming can be addressed through using Mixture Particle Filter (MPF) to model the target distribution as a non-parametric mixture model. According to computational complexity of the particle filter algorithm, Gaussian Particle Filter (GPF) is used. The particle set is obtained by sampling of Gaussian density function instead of re-sampling to improve the computing speed of particle filter algorithm. In this paper, combined with the advantages of GPF and MPF, GM-MPF algorithm is proposed to improve the real-time of MPF. The experimental results show that GM-MPF algorithm can effectively solve the problem of multi-modality, particle degradation and achieve real-time multi-target tracking. |
| Author | Liao, Dingan Yang, Yaping Wang, Shitong Lin, Qing Yin, Ying |
| Author_xml | – sequence: 1 givenname: Qing surname: Lin fullname: Lin, Qing organization: School of Computer Science and Telecommunications Engineering, Jiangsu University, Zhenjiang, 212013, China – sequence: 2 givenname: Yaping surname: Yang fullname: Yang, Yaping email: xgkx06s003@126.com, jcs533@qq.com organization: School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China – sequence: 3 givenname: Ying surname: Yin fullname: Yin, Ying organization: School of Information Technology, Southern Yangtze University, Wuxi 214122, China – sequence: 4 givenname: Shitong surname: Wang fullname: Wang, Shitong organization: School of Computer Science and Telecommunications Engineering, Jiangsu University, Zhenjiang, 212013, China – sequence: 5 givenname: Dingan surname: Liao fullname: Liao, Dingan organization: School of Computer Science and Telecommunications Engineering, Jiangsu University, Zhenjiang, 212013, China |
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| Keywords | Mixture Particle filter Gaussian Particle Filtering GM-MPF Particle filter Particle degradation |
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| References_xml | – reference: Comaniciu D, Ramesh V, Meer P, Kernel-based object tracking, J. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564-575. – reference: Doucet A, Godsill S, Andrieu C, On sequential monte carlo sampling methods for bayesian filtering, J. Statistics and Computing, 2000, 10(3): 197-208. – reference: Kotecha J H, Djuric P M, Gaussian particle filtering, J. IEEE Transactions on Signal Processing, 2003, 51(10): 2592-2601. – reference: Lin Qing, Xu Zliu, Wang Slii-Tong, Zlian Yong-Zliao, Moving Object Detection of Adaptive Gaussian Mixture on HSV, J Computer Science, 2010, 37(10): 254-256. – reference: Information on littp://ftp.pets.rdg.ac.uk!PETS2001/. – reference: Li Peihua, Moving object tracking method in image sequences, M. Science Press. – reference: Doucet, A, de Freitas, J.F. G., N.J. Gordon, editors: Sequential Monte Carlo Methods in Practice, M. Springer-Verlag, New York, 2001. – reference: R.E. Kalman, A new approach to linear filtering and prediction problems, Transactions of the ASME—Journal of Basic Engineering, J. 1960, 82: 35-45. – reference: Vermaak J, Doucet A, Perez P, Maintaining multi-modality mixture tracking, C. Proceedings oftlie Ninth IEEE International Conference on Computer Vision, Nice, 2003: 1110-1116. – ident: 10.1016/j.aasri.2012.11.030_bib0010 doi: 10.1007/978-1-4757-3437-9 – ident: 10.1016/j.aasri.2012.11.030_bib0005 doi: 10.1115/1.3662552 – ident: 10.1016/j.aasri.2012.11.030_bib0015 doi: 10.1023/A:1008935410038 – ident: 10.1016/j.aasri.2012.11.030_bib0020 doi: 10.1109/TPAMI.2003.1195991 – ident: 10.1016/j.aasri.2012.11.030_bib0025 – ident: 10.1016/j.aasri.2012.11.030_bib0030 doi: 10.1109/TSP.2003.816758 – ident: 10.1016/j.aasri.2012.11.030_bib0035 doi: 10.1109/ICCV.2003.1238473 – ident: 10.1016/j.aasri.2012.11.030_bib0040 – ident: 10.1016/j.aasri.2012.11.030_bib0045 |
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| SubjectTerms | Gaussian Particle Filtering GM-MPF Mixture Particle filter Particle degradation Particle filter |
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| Title | Improved Method Based on MPF for Multi-target Tracking |
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