Particle Filter Tracking Algorithm based on Dynamic Feature Fusion

DOI

10.2991/jiaet-18.2018.54How to use a DOI?

Keywords

Object tracking; Particle filter; Dynamic feature fusion; Object model

Abstract

A particle filter object tracking algorithm based on dynamic feature fusion is proposed in this paper. The presented algorithm uses the complementary features, which are gray histogram and gradient histogram, to represent the object model. In the tracking procession, the confidence for each feature is adjusted according to the discrimination between the object and the background, and the object model is dynamic online established and updated. The presented method can improve the accuracy of the object modeling and furthermore improve the accuracy of the particle filter tracking algorithm. Experimental results have demonstrated the effectiveness of our approach.

Copyright

© 2018, 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  - Heng Yang
AU  - Guojian Cheng
AU  - Hongchu Chen
PY  - 2018/03
DA  - 2018/03
TI  - Particle Filter Tracking Algorithm based on Dynamic Feature Fusion
BT  - Proceedings of the 2018 Joint International Advanced Engineering and Technology Research Conference (JIAET 2018)
PB  - Atlantis Press
SP  - 307
EP  - 311
SN  - 2352-5401
UR  - https://doi.org/10.2991/jiaet-18.2018.54
DO  - 10.2991/jiaet-18.2018.54
ID  - Yang2018/03
ER  -