基于预测的多特征融合Mean-Shift跟踪算法

计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 171-173.

基于预测的多特征融合Mean-Shift跟踪算法

郭宇,郝晓燕,张兴忠

太原理工大学太原030024

出版日期:2018-06-20 发布日期:2018-08-03
作者简介:郭宇(1994-),女,硕士生,CCF学生会员,主要研究方向为图形图像识别及处理,E-mail:646764407@qq.com;郝晓燕(1970-),女,博士,副教授,硕士生导师,主要研究方向为计算机语言学、图像处理;张兴忠(1964-),男,硕士,副教授,硕士生导师,主要研究方向为物联网与嵌入式系统应用、视频跟踪。
基金资助:
国网山西省电力公司科技项目(520530150015,5205301500W)资助

Multi-feature Fusion Mean-Shift Tracking Algorithm Based on Prediction

GUO Yu,HAO Xiao-yan,ZHANG Xing-zhong

Taiyuan University of Technology,Taiyuan 030024,China

Online:2018-06-20 Published:2018-08-03

摘要/Abstract

摘要： 视频监控在生活中的应用已经相当广泛,其中视频目标精确跟踪是计算机视觉中应用较广、难度较大的一部分。在实际视频场景中目标存在复杂的变化,如外形变化、部分遮挡、光照变化等,这对Mean-Shift跟踪算法产生了较大的影响。为了解决上述变化导致的跟踪不准确的问题,融合颜色和Gabor-LBP纹理特征进行Mean-Shift跟踪,并利用二次多项式预测运动目标的位置,以提高跟踪的准确度。

关键词: Mean-Shift, 二次多项式, 目标跟踪, 特征融合, 纹理特征

Abstract: The application of video surveillance in life has been quite extensive,especially the main target tracking is widely used in daily life,and it is a difficult part in the computer vision.In the real video scene,there are many complex target appearance changes,such as partial occlusion,light changes,etc.These have a greater impact on the Mean-Shift tracking algorithm.In order to solve the problem of inaccurate tracking caused by the above complex environment,this paper fused the color and Gabor-LBP edge features in Mean-Shift tracking algorithm,and introduced the quadratic polynomial to predict the position of the video target,to improve the tracking accuracy.

Key words: Feature fusion, Mean-Shift, Object tracking, Quadratic polynomial, Texture feature

中图分类号:

TP391

郭宇, 郝晓燕, 张兴忠. 基于预测的多特征融合Mean-Shift跟踪算法[J]. 计算机科学, 2018, 45(6A): 171-173. https://doi.org/

GUO Yu, HAO Xiao-yan, ZHANG Xing-zhong. Multi-feature Fusion Mean-Shift Tracking Algorithm Based on Prediction[J]. Computer Science, 2018, 45(6A): 171-173. https://doi.org/

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