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CLC number: TP317.4

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Received: 2008-09-07

Revision Accepted: 2008-12-22

Crosschecked: 2009-06-10

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.10 P.1476-1482


Embedding ensemble tracking in a stochastic framework for robust object tracking

Author(s):  Yu GU, Ping LI, Bo HAN

Affiliation(s):  Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   pli@iipc.zju.edu.cn

Key Words:  Ensemble tracking, Particle filter, Mean shift, Likelihood mean

Yu GU, Ping LI, Bo HAN. Embedding ensemble tracking in a stochastic framework for robust object tracking[J]. Journal of Zhejiang University Science A, 2009, 10(10): 1476-1482.

@article{title="Embedding ensemble tracking in a stochastic framework for robust object tracking",
author="Yu GU, Ping LI, Bo HAN",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

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%A Ping LI
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%DOI 10.1631/jzus.A0820647

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A1 - Yu GU
A1 - Ping LI
A1 - Bo HAN
J0 - Journal of Zhejiang University Science A
VL - 10
IS - 10
SP - 1476
EP - 1482
%@ 1673-565X
Y1 - 2009
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0820647

We propose an algorithm of embedding ensemble tracking in a stochastic framework to achieve robust tracking performance under partial occlusion, illumination changes, and abrupt motion. It operates on likelihood images generated by the ensemble method, and combines mean shift and particle filtering in a principled way, where a better proposal distribution is designed by first propagating particles via a motion model, and then running mean shift to move towards their local peaks in the likelihood image. An observation model in the particle filter incorporates global and local information within a region, and an adaptive motion model is adopted to depict the evolution of the object state. The algorithm needs fewer particles to manage the tracking task compared with the general particle filter, and recaptures the object quickly after occlusion occurs. Experiments on two image sequences demonstrate the effectiveness and robustness of the proposed algorithm.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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