A Novel Particle Filter based Object Tracking Framework via the Combination of State and Observation Optimization

DOI

10.2991/iccnce.2013.121How to use a DOI?

Keywords

particle filter, object tracking, feature optimization

Abstract

Using particle filter to figure visual object tracking, a key problem is to choose appropriate image features as the observation model. In this paper, we present a novel particle filter based object tracking framework via the combination of state and observation optimization. We apply the technique to articulated human movement tracking. Result demonstrates the effectiveness of our method in solving the tracking problem like self-occlusion and cluttered background.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xudong Luo
AU  - Long Ye
AU  - Wei Zhong
AU  - Qin Zhang
PY  - 2013/07
DA  - 2013/07
TI  - A Novel Particle Filter based Object Tracking Framework via the Combination of State and Observation Optimization
BT  - Proceedings of the International Conference on Computer, Networks and Communication Engineering (ICCNCE 2013)
PB  - Atlantis Press
SP  - 487
EP  - 490
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccnce.2013.121
DO  - 10.2991/iccnce.2013.121
ID  - Luo2013/07
ER  -