基于簇相似度的实时多尺度目标跟踪算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201603005

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Abstract To solve the problems of real-time object tracking and scale changing of the object in object tracking, a real-time object tracking algorithm is proposed based on cluster similarity measurement (MSCSM) in particle filtering framework. The improved average haar-like features are utilized to represent the proposed appearance model. Firstly, the target cluster and the background cluster are cropped in their sample radii. Secondly, a similarity measurement between a particle and a cluster is defined. The score of each particle is calculated according to its similarity with clusters while a new frame coming. Finally, the particle with the maximum score is selected as the new target location in the current frame. At the end of tracking for each frame, the statistical characteristics of clusters are updated and the particles are resampled to avoid degeneration.The proposed algorithm shows superiority in comparison with the state-of-the-art algorithms.

Key words： Object Tracking Standardized Euclidean Distance Haar-Like Feature Particle Filtering

Received: 12 May 2015

ZTFLH:

TP 391.4

Fund:Supported by National Natural Science Foundation of China (No.61472289), Natural Science Foundation of Hubei Province(No.2015CFB254)

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	Articles by authors
	LI Kang
	HE Fazhi
	CHEN Xiao
	PAN Yiteng
	YU Haiping

Cite this article:

LI Kang,HE Fazhi,CHEN Xiao等. Real-Time Multi-scale Object Tracking Based on Cluster Similarity[J]. , 2016, 29(3): 229-239.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201603005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I3/229

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