|Table of Contents|

A Moving Human Body Tracking Method Based onOptimized Gaussian Mixture Model(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2019年01期
Page:
51-
Research Field:
信息工程
Publishing date:

Info

Title:
A Moving Human Body Tracking Method Based onOptimized Gaussian Mixture Model
Author(s):
Shen Shibin1; Xie Fei1; 2; Niu Youchen3; Wang Tianyang1; 2; Zhong Ganglin1; 2; Gu Quanqi1
(1.School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210042,China)(2.Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing,Nanjing Normal University,Nanjing 210042,China)(3.China Eastern Airlines Jiangsu Limited Company,Nanjing 211113,China)
Keywords:
target tracking; Gaussian mixture model; optical flow method; Camshift algorithm
PACS:
TP391.4
DOI:
10.3969/j.issn.1672-1292.2019.01.007
Abstract:
The automatic detection and tracking effects of moving human target are always susceptible to the change of ambient light under the complex backgrounds. Aiming at the moving human target detection and tracking in variable lighting environments,the paper proposes a detection and tracking algorithm based on Camshift optimized by Gaussian mixture model. Firstly,the Gaussian mixture model is used to build the foreground model,and the external interference is settled as background information. Secondly,the color space is converted and backprojection values are calculated,and Meanshift iterative method is further used to locate the moving targets. Finally,the update of Gaussian mixture model and processing of subsequent frames can keep an effective detection and tracking of the moving human body. The experiment results show that the proposed algorithm has a better adaptive ability to the changing light of the ambient environment and the shaking of the foliage compared with traditional optical flow and Camshift methods. Besides,this algorithm can improve the detection and tracking accuracy of moving human body,and can better extract the foreground information of moving target.

References:

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Last Update: 2019-03-30