scholarly journals A rank constrained continuous formulation of multi-frame multi-target tracking problem

2008 ◽  
Author(s):  
Khurram Shafique ◽  
Mun Wai Lee ◽  
Niels Haering
Keyword(s):  
Target Tracking ◽  
Tracking Problem ◽  
Multi Target Tracking ◽  
Continuous Formulation

A Multi-Target Tracking Formulation of SVSF With the Joint Probabilistic Data Association Technique

2014 ◽  
Author(s):  
Mina Attari ◽  
S. Andrew Gadsden ◽  
Saeid R. Habibi
Keyword(s):  
Parameter Estimation ◽  
Target Tracking ◽  
Data Association ◽  
Joint Probability ◽  
Variable Structure ◽  
Tracking Problem ◽  
Proof Of Concept ◽  
Multi Target Tracking ◽  
Smooth Variable Structure Filter ◽  
Parameter Estimation Techniques

Target tracking scenarios offer an interesting challenge for state and parameter estimation techniques. This paper studies a situation with multiple targets in the presence of clutter. In this paper, the relatively new smooth variable structure filter (SVSF) is combined with the joint probability data association (JPDA) technique. This new method, referred to as the JPDA-SVSF, is applied on a simple multi-target tracking problem for a proof of concept. The results are compared with the popular Kalman filter (KF).

scholarly journals An Unbalanced Weighted Sequential Fusing Multi-Sensor GM-PHD Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 366
Author(s):  
Han Shen-Tu ◽  
Hanming Qian ◽  
Dongliang Peng ◽  
Yunfei Guo ◽  
Ji-An Luo
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Tracking Problem ◽  
Finite Sets ◽  
Probability Hypothesis Density ◽  
Multiple Sensors ◽  
Multi Target Tracking ◽  
Random Finite Sets

In this paper, we study the multi-sensor multi-target tracking problem in the formulation of random finite sets. The Gaussian Mixture probability hypothesis density (GM-PHD) method is employed to formulate the sequential fusing multi-sensor GM-PHD (SFMGM-PHD) algorithm. First, the GM-PHD is applied to multiple sensors to get the posterior GM estimations in a parallel way. Second, we propose the SFMGM-PHD algorithm to fuse the multi-sensor GM estimations in a sequential way. Third, the unbalanced weighted fusing and adaptive sequence ordering methods are further proposed for two improved SFMGM-PHD algorithms. At last, we analyze the proposed algorithms in four different multi-sensor multi-target tracking scenes, and the results demonstrate the efficiency.

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

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
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