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Efficient and robust fragments-based multiple kernels tracking
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AI
DOI：10.1016/j.aeue.2011.02.013.png)
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Abstract
En 中文
Representing an object with multiple image fragments or patches for target tracking in a video has proved to be able to maintain the spatial information. The major challenges in visual tracking are effectiveness and robustness. In this paper, we propose an efficient and robust fragments-based multiple kernels tracking algorithm. Fusing the log-likelihood ratio image and morphological operation divides the object into some fragments, which can maintain the spatial information. By assigning each fragment to different weight, more robust target and candidate models are built. Applying adaptive scale selection and updating schema for the target model and the weighting factors of each fragment can improve tracking robustness. Upon these advantages, the novel tracking algorithm can provide more accurate performance and can be directly extended to a multiple object tracking system. (C) 2011 Elsevier GmbH. All rights reserved.
Keywords:
Multiple kernels tracking
Object tracking
Adaptive scale selection
Mean shift
Journal
A
IF:
3.2
Papers:
5.6K
Citations:
8.3K