Joint PHD Filter and Hungarian Assignment Algorithm for Multitarget Tracking in Low Signal-to-Noise Ratio
Multitarget tracking (MTT) for image processing in low signal-to-noise ratio (SNR) is difficult and computationally expensive because the distinction between the target and the background is small. Among the current MTT algorithms, Random Finite Set (RFS) based filters are computationally tractable....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Spolecnost pro radioelektronicke inzenyrstvi
2023-06-01
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| Series: | Radioengineering |
| Subjects: | |
| Online Access: | https://www.radioeng.cz/fulltexts/2023/23_02_0287_0297.pdf |
| _version_ | 1797815105808760832 |
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| author | S. Xiao H. Tao X. Shen L. Zhang M. Hu |
| author_facet | S. Xiao H. Tao X. Shen L. Zhang M. Hu |
| author_sort | S. Xiao |
| collection | DOAJ |
| description | Multitarget tracking (MTT) for image processing in low signal-to-noise ratio (SNR) is difficult and computationally expensive because the distinction between the target and the background is small. Among the current MTT algorithms, Random Finite Set (RFS) based filters are computationally tractable. However, the probability hypothesis density (PHD) filter, despite its low computational complexity, is not suitable for MTT in low SNR. The generalized labeled multi-Bernoulli (GLMB) filter and its fast implementation are unsuitable for realtime MTT due to their high computational complexity. To achieve realtime MTT in low SNR, a joint PHD filter and Hungarian assignment algorithm is first proposed in this work. The PHD filter is used for preliminary tracking of targets while the Hungarian assignment algorithm is employed to complete the association process. To improve the tracking performance in low SNR, a new track must undergo a trial period and a valid track will be terminated only if it is not detected for several frames. The simulation results show that the proposed MTT algorithm can achieve stable tracking performance in low SNR with small computational complexity. The proposed filter can be applied to MTT in low SNR that require realtime implementation. |
| first_indexed | 2024-03-13T08:17:46Z |
| format | Article |
| id | doaj.art-762f438eda1048438f223c2701a2808a |
| institution | Directory Open Access Journal |
| issn | 1210-2512 |
| language | English |
| last_indexed | 2024-03-13T08:17:46Z |
| publishDate | 2023-06-01 |
| publisher | Spolecnost pro radioelektronicke inzenyrstvi |
| record_format | Article |
| series | Radioengineering |
| spelling | doaj.art-762f438eda1048438f223c2701a2808a2023-05-31T12:58:07ZengSpolecnost pro radioelektronicke inzenyrstviRadioengineering1210-25122023-06-01322287297Joint PHD Filter and Hungarian Assignment Algorithm for Multitarget Tracking in Low Signal-to-Noise RatioS. XiaoH. TaoX. ShenL. ZhangM. HuMultitarget tracking (MTT) for image processing in low signal-to-noise ratio (SNR) is difficult and computationally expensive because the distinction between the target and the background is small. Among the current MTT algorithms, Random Finite Set (RFS) based filters are computationally tractable. However, the probability hypothesis density (PHD) filter, despite its low computational complexity, is not suitable for MTT in low SNR. The generalized labeled multi-Bernoulli (GLMB) filter and its fast implementation are unsuitable for realtime MTT due to their high computational complexity. To achieve realtime MTT in low SNR, a joint PHD filter and Hungarian assignment algorithm is first proposed in this work. The PHD filter is used for preliminary tracking of targets while the Hungarian assignment algorithm is employed to complete the association process. To improve the tracking performance in low SNR, a new track must undergo a trial period and a valid track will be terminated only if it is not detected for several frames. The simulation results show that the proposed MTT algorithm can achieve stable tracking performance in low SNR with small computational complexity. The proposed filter can be applied to MTT in low SNR that require realtime implementation.https://www.radioeng.cz/fulltexts/2023/23_02_0287_0297.pdfhungarian assignment algorithmphd filtermultitarget tracking (mtt)low signal-to-noise ratio (snr) |
| spellingShingle | S. Xiao H. Tao X. Shen L. Zhang M. Hu Joint PHD Filter and Hungarian Assignment Algorithm for Multitarget Tracking in Low Signal-to-Noise Ratio Radioengineering hungarian assignment algorithm phd filter multitarget tracking (mtt) low signal-to-noise ratio (snr) |
| title | Joint PHD Filter and Hungarian Assignment Algorithm for Multitarget Tracking in Low Signal-to-Noise Ratio |
| title_full | Joint PHD Filter and Hungarian Assignment Algorithm for Multitarget Tracking in Low Signal-to-Noise Ratio |
| title_fullStr | Joint PHD Filter and Hungarian Assignment Algorithm for Multitarget Tracking in Low Signal-to-Noise Ratio |
| title_full_unstemmed | Joint PHD Filter and Hungarian Assignment Algorithm for Multitarget Tracking in Low Signal-to-Noise Ratio |
| title_short | Joint PHD Filter and Hungarian Assignment Algorithm for Multitarget Tracking in Low Signal-to-Noise Ratio |
| title_sort | joint phd filter and hungarian assignment algorithm for multitarget tracking in low signal to noise ratio |
| topic | hungarian assignment algorithm phd filter multitarget tracking (mtt) low signal-to-noise ratio (snr) |
| url | https://www.radioeng.cz/fulltexts/2023/23_02_0287_0297.pdf |
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