Object tracking algorithm based on static-adaptive appearance model correction

doi:10.11772/j.issn.1001-9081.2017092312

Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (4): 1170-1175.DOI: 10.11772/j.issn.1001-9081.2017092312

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Object tracking algorithm based on static-adaptive appearance model correction

WEI Baoguo¹, GE Ping¹, WU Hong¹, WANG Gaofeng², HAN Wenliang³

1. College of Electronic Information, Northwestern Polytechnical University, Xi'an Shaanxi 710129, China;
2. Guizhou Yu Peng Technology Company Limited, Guiyang Guizhou 550000, China;
3. Unit 93108, the Chinese People's Liberation Army, Qiqihar Heilongjiang 161000, China

Received:2017-09-25 Revised:2017-12-04 Online:2018-04-10 Published:2018-04-09
Supported by:
This work is partially supported by the Key Science and Technology Program of Guizhou Province (2017GZ60903), the Science and Technology Research Program of Xi'an City (2017086CG/RC049).

基于静态自适应外观模型纠正的目标跟踪算法

卫保国¹, 葛苹¹, 武宏¹, 王高峰², 韩文亮³

1. 西北工业大学电子信息学院, 西安 710129;
2. 贵州宇鹏科技有限责任公司, 贵阳 550000;
3. 中国人民解放军93108部队, 黑龙江齐齐哈尔 161000

通讯作者: 卫保国
作者简介:卫保国(1970-),男,陕西乾县人,副教授,博士,主要研究方向:图像处理、模式识别、计算机视觉;葛苹(1992-),女,山西大同人,硕士研究生,主要研究方向:目标检测与跟踪。
基金资助:
贵州省科技厅工业攻关计划项目（2017GZ60903）；西安市科技计划项目（2017086CG/RC049）。

Abstract

Abstract: For long-term robust tracking to single target, a corrected tracking algorithm based on static-adaptive appearance model was proposed. Firstly, the interference factors that may be encountered in the tracking process were divided into two categories from environment and target itself, then a static appearance model and an adaptive appearance model were proposed respectively. The static appearance model was used for global matching while the adaptive appearance model was employed for local tracking, and the former corrected tracking drift of the latter. A single-link hierarchical clustering algorithm was used to remove the noise introduced by the fusion of the above two models. To capture the re-occurring target, static appearance model was applied for global search. Experimental results on standard video sequences show that the accuracy of tracking the target center is 0.9, and the computer can process 26 frames per second. The proposed tracking algorithm framework can achieve long-term stable tracking with good robustness and real-time performance.

Key words: object tracking, static-adaptive appearance model, hierarchical clustering, tracking algorithm framework

摘要： 针对单目标跟踪过程中难以长期稳定跟踪的问题，提出一种基于静态-自适应外观模型纠正的跟踪算法。首先将跟踪过程中可能遇到的干扰因素分为来自环境和目标本身两类，分别提出静态外观模型和自适应外观模型，静态外观模型用于全局匹配，自适应外观模型用于局部跟踪，静态模型纠正自适应模型的跟踪漂移问题；使用单链接层次聚类算法去除两种模型融合后引入的噪声；针对运动目标消失再出现时难以捕获的问题，使用静态模型进行全局搜索，捕获目标。对于实验中的视频序列，视频序列中目标的中心位置准确率为0.9，计算机每秒能够处理26帧图像。实验结果表明，该跟踪算法框架可以实现长期稳定的跟踪，具有良好的鲁棒性和实时性。

关键词: 目标跟踪, 静态-自适应外观模型, 层次聚类, 跟踪算法框架

CLC Number:

TP391.41

WEI Baoguo, GE Ping, WU Hong, WANG Gaofeng, HAN Wenliang. Object tracking algorithm based on static-adaptive appearance model correction[J]. Journal of Computer Applications, 2018, 38(4): 1170-1175.

卫保国, 葛苹, 武宏, 王高峰, 韩文亮. 基于静态自适应外观模型纠正的目标跟踪算法[J]. 计算机应用, 2018, 38(4): 1170-1175.

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Object tracking algorithm based on static-adaptive appearance model correction

基于静态自适应外观模型纠正的目标跟踪算法

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