A Novel HRBW-based approach to Mean shift algorithm for Target Tracking
Xiaowei Wang, Yahui Han, Antong Gao
Available Online March 2017.
- https://doi.org/10.2991/ifmca-16.2017.52How to use a DOI?
- Target Tracking; Mean Shift; Background Weighted; Corrected Background Weighted; Histogram Ration; Histogram Ration Background Weighted
- To resolve the problem of localization errors in object tracking, which caused by the background pixels in an object model, a novel target tracking algorithm named Histogram Ration Background Weighted-based mean shift (HRBWBMS)is presented. In the proposed HRBWMS, unlike the standard mean shift, the target model is established based on object/background histogram log-likelihood ratio. A new weight transform method for target model based on object/background histogram log-likelihood ratio was introduced. The experimental results show that the proposed method not only accelerates the convergence, but also enhances anti-interference ability.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Xiaowei Wang AU - Yahui Han AU - Antong Gao PY - 2017/03 DA - 2017/03 TI - A Novel HRBW-based approach to Mean shift algorithm for Target Tracking BT - Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016) PB - Atlantis Press SP - 330 EP - 336 SN - 2352-5401 UR - https://doi.org/10.2991/ifmca-16.2017.52 DO - https://doi.org/10.2991/ifmca-16.2017.52 ID - Wang2017/03 ER -