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Style Neutralization Generative Adversarial Classifier Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Salient Superpixel Visual Tracking with Coarse-to-Fine Segmentation and Manifold Ranking

Authors : Jin Zhan, Huimin Zhao

Published in: Advances in Brain Inspired Cognitive Systems

Publisher: Springer International Publishing

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Abstract

We propose a novel salient superpixel based tracking algorithm using Coarse-to-Fine segmentation on graph model, where target state is estimated by a combination of pixel-level cues and middle-level cues to achieve accurate target appearance model. We exploit temporal optical flow and color distribution characteristics as coarse grained information from pixel-level processing, and propagate to fine-grained superpixels to improve initial target appearance segmentation from bounding box annotations. Our algorithm constructs a graph model with manifold ranking by improved superpixels to estimate the saliency of target foreground and background in subsequent frames. The tracking result is located by calculating the weight of multi-scale box, where the weight depends on the similarity of scores of foreground and background superpixels in the scale box. We compared our algorithm with the existing techniques in OTB100 dataset, and achieved substantially better performance.

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previous chapter Fabric Defect Detection Based on Sparse Representation Image Decomposition
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Metadata
Title
Salient Superpixel Visual Tracking with Coarse-to-Fine Segmentation and Manifold Ranking
Authors
Jin Zhan
Huimin Zhao
Copyright Year
2018
Book
Advances in Brain Inspired Cognitive Systems

Print ISBN: 978-3-030-00562-7

Electronic ISBN: 978-3-030-00563-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00563-4_42

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