Object Tracking Based on Corrected Background-Weighted Histogram Mean Shift and Kalman Filter
- Yu Yang, Yongxing Jia, Chuanzhen Rong, Ying Zhu, Yuan Wang, Zhenjun Yue, Zhenxing Gao
- Corresponding Author
- Yu Yang
Available Online April 2013.
- https://doi.org/10.2991/icsem.2013.140How to use a DOI?
- object tracking, mean shift, background information, Kalman filter
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 2nd International Conference On Systems Engineering and Modeling (ICSEM-13) PB - Atlantis Press UR - https://doi.org/10.2991/icsem.2013.140 DO - https://doi.org/10.2991/icsem.2013.140 ID - Yang2013/04 ER -