[1]LIU Cuijun,ZHAO Cairong,MIAO Duoqian,et al.Granular mean shift pedestrian tracking algorithm[J].CAAI Transactions on Intelligent Systems,2016,11(4):433-441.[doi:10.11992/tis.201605033]
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Granular mean shift pedestrian tracking algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 4 Page number: 433-441 Column: 学术论文—机器学习 Public date: 2016-07-25
Title:
Granular mean shift pedestrian tracking algorithm
Author(s):
LIU Cuijun;  ZHAO Cairong;  MIAO Duoqian;  WANG Xuekuan
College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
Keywords:
information granules; granular computing; mean shift; feature extraction; pedestrian tracking
CLC:
TP391
DOI:
10.11992/tis.201605033
Abstract:
Mean shift pedestrian tracking that uses a color histogram as its tracking feature has drawbacks, e.g., performance can easily be affected by the introduction of a background color. To solve this problem, the idea of granular computing was introduced into the traditional mean shift pedestrian tracking algorithm, and a new granular mean shift pedestrian tracking algorithm, based on granular computing, is presented. The algorithm blocks the image’s target area with specific granularity to extract color features, then adopts different color channels of granulation on the feature, and finally realizes target tracking under the framework of the mean shift iteration. Compared with other traditional methods the algorithm displays lower computational complexity and is more robust. Experimental results on PETS2009 and CAVIAR databases show that the algorithm achieves a higher tracking accuracy, better robustness and efficiency under color interference, and can track the target pedestrian in real time.
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