Performance Analysis of Sequential Monte Carlo MCMC and PHD Filters on Multi-target Tracking in Video

2014 ◽  
Author(s):  
Abdullahi Daniyan
Keyword(s):  
Monte Carlo ◽  
Performance Analysis ◽  
Target Tracking ◽  
Sequential Monte Carlo ◽  
Multi Target Tracking

scholarly journals A Computationally Efficient Labeled Multi-Bernoulli Smoother for Multi-Target Tracking

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4226 ◽  
Author(s):  
Rang Liu ◽  
Hongqi Fan ◽  
Tiancheng Li ◽  
Huaitie Xiao
Keyword(s):  
Monte Carlo ◽  
Computational Complexity ◽  
Monte Carlo Method ◽  
State Estimation ◽  
Target Tracking ◽  
Computational Efficiency ◽  
Sequential Monte Carlo ◽  
Computationally Efficient ◽  
Sequential Monte Carlo Method ◽  
Multi Target Tracking

A forward–backward labeled multi-Bernoulli (LMB) smoother is proposed for multi-target tracking. The proposed smoother consists of two components corresponding to forward LMB filtering and backward LMB smoothing, respectively. The former is the standard LMB filter and the latter is proved to be closed under LMB prior. It is also shown that the proposed LMB smoother can improve both the cardinality estimation and the state estimation, and the major computational complexity is linear with the number of targets. Implementation based on the Sequential Monte Carlo method in a representative scenario has demonstrated the effectiveness and computational efficiency of the proposed smoother in comparison to existing approaches.

scholarly journals A Robust SMC-PHD Filter for Multi-Target Tracking with Unknown Heavy-Tailed Measurement Noise

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3611
Author(s):  
Yang Gong ◽  
Chen Cui
Keyword(s):  
Target Tracking ◽  
Sequential Monte Carlo ◽  
Measurement Noise ◽  
Particle Simulation ◽  
Target Number ◽  
Student T Distribution ◽  
Multi Target Tracking ◽  
Heavy Tailed ◽  
Scale Matrix ◽  
T Distribution

In multi-target tracking, the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter is a practical algorithm. Influenced by outliers under unknown heavy-tailed measurement noise, the SMC-PHD filter suffers severe performance degradation. In this paper, a robust SMC-PHD (RSMC-PHD) filter is proposed. In the proposed filter, Student-t distribution is introduced to describe the unknown heavy-tailed measurement noise where the degrees of freedom (DOF) and the scale matrix of the Student-t distribution are respectively modeled as a Gamma distribution and an inverse Wishart distribution. Furthermore, the variational Bayesian (VB) technique is employed to infer the unknown DOF and scale matrix parameters while the recursion estimation framework of the RSMC-PHD filter is derived. In addition, considering that the introduced Student- t distribution might lead to an overestimation of the target number, a strategy is applied to modify the updated weight of each particle. Simulation results demonstrate that the proposed filter is effective with unknown heavy-tailed measurement noise.

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