Object tracking using both a kernel and a non-parametric active contour model

By combining both a kernel-based tracking and a non-parametric level set method, a novel framework for target tracking is proposed in this paper that robustly addresses tracking fast-moving and small targets with blurred edges. To establish our new framework, Kullback–Leibler divergence was adopted...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2018-06, Vol.295, p.108-117
Main Authors: Hang, Yu, Derong, Chen, Jiulu, Gong
Format: Article
Language:English
Subjects:
Active contour
Kernel tracking
Optimization
Segmentation
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Summary:By combining both a kernel-based tracking and a non-parametric level set method, a novel framework for target tracking is proposed in this paper that robustly addresses tracking fast-moving and small targets with blurred edges. To establish our new framework, Kullback–Leibler divergence was adopted to measure the divergence between the foreground/background distributions and the target model, and the Bhattacharyya distance was adopted to measure the similarities between the foreground and background distributions. An image warping matrix is introduced into the framework to optimize the target function. The experimental results demonstrate the advantages of the proposed method compared with other methods.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2018.03.021