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2016 | OriginalPaper | Chapter

Visual Tracking by Local Superpixel Matching with Markov Random Field

Authors : Heng Fan, Jinhai Xiang, Zhongmin Chen

Published in: Advances in Multimedia Information Processing - PCM 2016

Publisher: Springer International Publishing

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Abstract

In this paper, we propose a novel method to track non-rigid and/or articulated objects using superpixel matching and markov random field (MRF). Our algorithm consists of three stages. First, a superpixel dataset is constructed by segmenting training frames into superpixels, and each superpixel is represented by multiple features. The appearance information of target is encoded in the superpixel database. Second, each new frame is segmented into superpixels and then its object-background confidence map is derived by comparing its superpixels with k-nearest neighbors in superpixel dataset. Taking context information into account, we utilize MRF to further improve the accuracy of confidence map. In addition, the local context information is incorporated through a feedback to refine superpixel matching. In the last stage, visual tracking is achieved via finding the best candidate by maximum a posterior estimate based on the confidence map. Experiments show that our method outperforms several state-of-the-art trackers.

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next chapter Saliency Detection Combining Multi-layer Integration Algorithm with Background Prior and Energy Function
Footnotes
1
The surrounding region is a square area centered at the location of target \(X_{t}^{c}\), and its side length is equal to \(\lambda _{s}[X_{s}^{t}]^{\frac{1}{2}}\), where \(X_{t}^{c}\) represents the center location of target region \(X_{t}\) and \(X_{t}^{s}\) denotes its size. The parameter \(\lambda _{s}\) is a constant variable, which determines the size of this surrounding region.
 
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Metadata
Title
Visual Tracking by Local Superpixel Matching with Markov Random Field
Authors
Heng Fan
Jinhai Xiang
Zhongmin Chen
Copyright Year
2016
Book
Advances in Multimedia Information Processing - PCM 2016

Print ISBN: 978-3-319-48889-9

Electronic ISBN: 978-3-319-48890-5

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-48890-5_1