A Novel HRBW-based approach to Mean shift algorithm for Target Tracking

DOI

10.2991/ifmca-16.2017.52How to use a DOI?

Keywords

Target Tracking; Mean Shift; Background Weighted; Corrected Background Weighted; Histogram Ration; Histogram Ration Background Weighted

Abstract

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.

Copyright

© 2017, 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  - 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  - 10.2991/ifmca-16.2017.52
ID  - Wang2017/03
ER  -