Gaussian Approximation for Tracking Occluding and Interacting Targets

被引:2
|
作者
Medrano, Carlos [1 ]
Martinez, Jesus [1 ]
Igual, Raul [2 ]
Elias Herrero, Jose [1 ]
Orrite, Carlos [1 ]
机构
[1] Aragon Inst Engn Res, Comp Vis Lab, Zaragoza 50018, Spain
[2] Univ Zaragoza, EduQTech, EU Politecn Teruel, Teruel 44003, Spain
关键词
Multi-target tracking; Gaussian approximation; Kalman filter; Occlusion; Markov random field; Articulated object tracking; FILTERS;
D O I
10.1007/s10851-009-0183-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we show how interacting and occluding targets can be tackled successfully within a Gaussian approximation. For that purpose, we develop a general expansion of the mean and covariance of the posterior and we consider a first order approximation of it. The proposed method differs from EKF in that neither a non-linear dynamical model nor a non-linear measurement vector to state relation have to be defined, so it works with any kind of interaction potential and likelihood. The approach has been tested on three sequences (10400, 2500, and 400 frames each one). The results show that our approach helps to reduce the number of failures without increasing too much the computation time with respect to methods that do not take into account target interactions.
引用
收藏
页码:241 / 253
页数:13
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