Object Tracking Based on Corrected Background-Weighted Histogram Mean Shift and Kalman Filter

DOI

10.2991/icsem.2013.140How to use a DOI?

Keywords

object tracking, mean shift, background information, Kalman filter

Abstract

The classical mean shift (MS) algorithm is the best color-based method for object tracking. However, in the real environment it presents some limitations, especially under the presence of noise, objects with partial and full occlusions in complex environments. In order to deal with these problems, this paper proposes a reliable object tracking algorithm using corrected background-weighted histogram (CBWH) and the Kalman filter (KF) based on the MS method. The experimental results show that the proposed method is superior to the traditional MS tracking in the following aspects: 1) it provides consistent object tracking throughout the video; 2) it is not influenced by the objects with partial and full occlusions; 3) it is less prone to the background clutter.

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  - Yu Yang
AU  - Yongxing Jia
AU  - Chuanzhen Rong
AU  - Ying Zhu
AU  - Yuan Wang
AU  - Zhenjun Yue
AU  - Zhenxing Gao
PY  - 2013/04
DA  - 2013/04
TI  - Object Tracking Based on Corrected Background-Weighted Histogram Mean Shift and Kalman Filter
BT  - Proceedings of the 2nd International Conference On Systems Engineering and Modeling (ICSEM 2013)
PB  - Atlantis Press
SP  - 687
EP  - 692
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsem.2013.140
DO  - 10.2991/icsem.2013.140
ID  - Yang2013/04
ER  -