DocumentCode :
684075
Title :
Multi-object tracking based on improved Mean Shift
Author :
Meifeng Gao ; Di Liu
Author_Institution :
Key Lab. of Adv. Process Control for Light Ind., Jiangnan Univ., Wuxi, China
fYear :
2013
fDate :
23-25 March 2013
Firstpage :
1588
Lastpage :
1592
Abstract :
Since Mean Shift algorithm can not track multiple objects, a full automatic multi-object tracking algorithm based on improved Mean Shift is proposed. The background subtraction image kernel density estimation algorithm is used to detect the foreground. The extracted moving objects are used as candidate template to eliminate the influence of background. By adopting object matching based on distance matrix, new objects entering to the scene and occlusion-split between objects could be handled. The tracking accuracy is increased by using shadow removal and morphology processing. The experiment results show that the proposed method can achieve multiple-object tracking accurately, and deal with the occlusion-split between objects very well.
Keywords :
computer vision; feature extraction; image matching; image motion analysis; matrix algebra; object detection; object tracking; background subtraction image kernel density estimation algorithm; candidate template; distance matrix; foreground detection; full automatic multiobject tracking algorithm; improved mean shift; mean shift algorithm; morphology processing; moving objects extraction; object matching; object occlusion-split; shadow removal; tracking accuracy; Arrays; Histograms; Image color analysis; Kernel; Object tracking; Target tracking;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Technology (ICIST), 2013 International Conference on
Conference_Location :
Yangzhou
Print_ISBN :
978-1-4673-5137-9
Type :
conf
DOI :
10.1109/ICIST.2013.6747840
Filename :
6747840
Link To Document :
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