Multi-target tracking with an event-based vision sensor and a partial-update GMPHD filter

2019 ◽  
Vol 2019 (13) ◽  
pp. 127-1-127-7
Author(s):  
Benjamin J. Foster ◽  
Dong Hye Ye ◽  
Charles A. Bouman
Keyword(s):  
Target Tracking ◽  
Vision Sensor ◽  
Multi Target Tracking ◽  
Event Based

Event-Based Target Tracking Control for a Snake Robot Using a Dynamic Vision Sensor

2017 ◽  
pp. 111-121 ◽  
Author(s):  
Zhuangyi Jiang ◽  
Zhenshan Bing ◽  
Kai Huang ◽  
Guang Chen ◽  
Long Cheng ◽  
...  
Keyword(s):  
Target Tracking ◽  
Tracking Control ◽  
Vision Sensor ◽  
Snake Robot ◽  
Dynamic Vision ◽  
Event Based

Shared-memory parallel multi-target tracking

2009 ◽  
Vol 28 (9) ◽  
pp. 2303-2305
Author(s):  
Xiao-gang WANG ◽  
Xiao-juan WU ◽  
Xin ZHOU ◽  
Xiao-yan ZHANG
Keyword(s):  
Target Tracking ◽  
Shared Memory ◽  
Multi Target Tracking

Multi-target tracking algorithm based on FIR filters

2014 ◽  
Author(s):  
Chang Joo Lee ◽  
Kyung Min Min ◽  
Hyun Duck Choi ◽  
Choon Ki Ahn ◽  
Myo Taeg Lim
Keyword(s):  
Target Tracking ◽  
Tracking Algorithm ◽  
Fir Filters ◽  
Multi Target Tracking

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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