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FISST based method for multi-target tracking in the image plane of optical sensors.

Xu Y, Xu H, An W, Xu D - Sensors (Basel) (2012)

Bottom Line: Then the PHD recursion equations incorporated with signal information are derived and the Gaussian mixture (GM) implementation of this filter is given.Simulation results demonstrate that the proposed method achieves significantly better performance than the generic PHD filter.Moreover, our method has much lower computational complexity in the scenario with high SNR and dense clutter.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China. xuyang012@nudt.edu.cn

ABSTRACT
A finite set statistics (FISST)-based method is proposed for multi-target tracking in the image plane of optical sensors. The method involves using signal amplitude information in probability hypothesis density (PHD) filter which is derived from FISST to improve multi-target tracking performance. The amplitude of signals generated by the optical sensor is modeled first, from which the amplitude likelihood ratio between target and clutter is derived. An alternative approach is adopted for the situations where the signal noise ratio (SNR) of target is unknown. Then the PHD recursion equations incorporated with signal information are derived and the Gaussian mixture (GM) implementation of this filter is given. Simulation results demonstrate that the proposed method achieves significantly better performance than the generic PHD filter. Moreover, our method has much lower computational complexity in the scenario with high SNR and dense clutter.

No MeSH data available.


Average computation time per frame for different algorithms with different SNRs d and probabilities of false alarm .
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f4-sensors-12-02920: Average computation time per frame for different algorithms with different SNRs d and probabilities of false alarm .

Mentions: From Table 2 we see that for all combinations of and d given(corresponding to different probabilities of detection), our AI-PHD filter both with known and unknown SNR gives better performance than the generic PHD filter. This improvement is enhanced as or d increases. In the case of and d = 8 where the method using the amplitude information works best, our AI-PHD filter achieves 15.94 and 12.91 lower average OSPA (pixel) for known and unknown SNR, respectively. This improvement in performance is mainly due to two reasons: firstly, as d increases, the false alarm distribution will poorly represent the target counterpart and there is a big distinction in the target and false alarm distributions; secondly, as increases, having more measurements aids the method using the amplitude, since we discard less useful information. Table 2 also shows that the performance of generic PHD filter without amplitude decreases rapidly as increases since there are more measurements from false alarms which by no means could be identified from those from targets. In contrast, we see no deterioration in the performance of AI-PHD filter in this case. For the known SNR case especially, the performance increases consistently as increases, which means our method works even better in a scenario with dense clutters. The comparison of computational complexity between AI-PHD filter and generic PHD filter without amplitude information is shown in average computation time per frame versus target SNR for different in Figure 4. Since we can achieve similar complexity for unknown SNR case with that of known SNR by computing Equation (27) with some fast algorithms, only the result for the known SNR case is given.


FISST based method for multi-target tracking in the image plane of optical sensors.

Xu Y, Xu H, An W, Xu D - Sensors (Basel) (2012)

Average computation time per frame for different algorithms with different SNRs d and probabilities of false alarm .
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376575&req=5

f4-sensors-12-02920: Average computation time per frame for different algorithms with different SNRs d and probabilities of false alarm .
Mentions: From Table 2 we see that for all combinations of and d given(corresponding to different probabilities of detection), our AI-PHD filter both with known and unknown SNR gives better performance than the generic PHD filter. This improvement is enhanced as or d increases. In the case of and d = 8 where the method using the amplitude information works best, our AI-PHD filter achieves 15.94 and 12.91 lower average OSPA (pixel) for known and unknown SNR, respectively. This improvement in performance is mainly due to two reasons: firstly, as d increases, the false alarm distribution will poorly represent the target counterpart and there is a big distinction in the target and false alarm distributions; secondly, as increases, having more measurements aids the method using the amplitude, since we discard less useful information. Table 2 also shows that the performance of generic PHD filter without amplitude decreases rapidly as increases since there are more measurements from false alarms which by no means could be identified from those from targets. In contrast, we see no deterioration in the performance of AI-PHD filter in this case. For the known SNR case especially, the performance increases consistently as increases, which means our method works even better in a scenario with dense clutters. The comparison of computational complexity between AI-PHD filter and generic PHD filter without amplitude information is shown in average computation time per frame versus target SNR for different in Figure 4. Since we can achieve similar complexity for unknown SNR case with that of known SNR by computing Equation (27) with some fast algorithms, only the result for the known SNR case is given.

Bottom Line: Then the PHD recursion equations incorporated with signal information are derived and the Gaussian mixture (GM) implementation of this filter is given.Simulation results demonstrate that the proposed method achieves significantly better performance than the generic PHD filter.Moreover, our method has much lower computational complexity in the scenario with high SNR and dense clutter.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China. xuyang012@nudt.edu.cn

ABSTRACT
A finite set statistics (FISST)-based method is proposed for multi-target tracking in the image plane of optical sensors. The method involves using signal amplitude information in probability hypothesis density (PHD) filter which is derived from FISST to improve multi-target tracking performance. The amplitude of signals generated by the optical sensor is modeled first, from which the amplitude likelihood ratio between target and clutter is derived. An alternative approach is adopted for the situations where the signal noise ratio (SNR) of target is unknown. Then the PHD recursion equations incorporated with signal information are derived and the Gaussian mixture (GM) implementation of this filter is given. Simulation results demonstrate that the proposed method achieves significantly better performance than the generic PHD filter. Moreover, our method has much lower computational complexity in the scenario with high SNR and dense clutter.

No MeSH data available.