Multi-target Tracking and Track Management Algorithm Based on UFIR Filter With Imperfect Detection Probability
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This paper proposes an unbiased finite impulse response filter and track management algorithm for multi-target tracking (MTT) with imperfect detection probability. Targets cannot be detected under MTT for various reasons, including sensor failure and screening by other targets. Despite the temporary missed detection, the proposed MTT algorithm robustly tracks targets under MTT conditions by replacing the missed detection with recently detected target measurement. The track is deleted on the track table when consecutive detection failure exceeding missing horizon occurs. Computational time for the proposed MTT algorithm is significantly less than that for existing MTT algorithm based finite impulse response filters due to the proposed track update and track management algorithm. Simulation and experimental vehicle and pedestrian tracking results verify outstanding tracking accuracy and shorter calculation times for the proposed algorithm.
KeywordsFinite impulse response structure imperfect detection probability missing horizon multi-target tracking track management unbiased filtering
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- Y. T. Tesfaye, E. Zemene, A. Prati, M. Pelillo, and M. Shah, “Multi-target tracking in multiple non-overlapping cameras using constrained dominant sets,” arXiv preprint arXiv:1706.06196, 2017.Google Scholar
- S. H. Rezatofighi, S. Gould, B. T. Vo, B.-N. Vo, K. Mele, and R. Hartley, “Multi-target tracking with time-varying clutter rate and detection profile: application to time-lapse cell microscopy sequences,” IEEE Transactions on Medical Imaging, vol. 34, no. 6, pp. 1336–1348, 2015.CrossRefGoogle Scholar
- S. H. You, C. K. Ahn, Y. S. Shmaliy, and S. Zhao, “Minimum weighted Frobenius norm discrete-time FIR filter with embedded unbiasedness,” IEEE Transactions on Circuits and Systems II: Express Briefs, 2018.Google Scholar
- W. H. Kwon and S. H. Han, Receding Horizon Control: Model Predictive Control for State Models, Springer Science & Business Media, 2006.Google Scholar
- L. Stanislas and T. Peynot, “Characterisation of the Delphi electronically scanning radar for robotics applications,” Proc. of ARAA Australasian Conference on Robotics and Automation, ARAA, 2015.Google Scholar
- X. Wei, Z. Guo, M. Guo, and X. Liu, “Disturbance observer-based anti-disturbance control for a class of stochastic systems,” Proc. of Chinese Control Conference (CCC), pp. 2860–2864, 2015.Google Scholar