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An Evolution Perception Mechanism for Organizing and Displaying 3D Shape Collections Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Correlation Filters Tracker Based on Two-Level Filtering Edge Feature

Authors : Dengzhuo Zhang, Donglan Cai, Yun Gao, Hao Zhou, Tianwen Li

Published in: Computer Vision

Publisher: Springer Singapore

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Abstract

In recent years, correlation filter frame has attracted more attention in visual object tracking, providing a real-time way. However, with the increase of the computing complexity of feature extractor, the trackers lost the real-time advantage of correlation filters. Moreover, correlation filters model drift can result in tracking failure. In order to solve these problems, a novel and simple correlation filters tracker based on two-level filtering edge feature (ECFT) was proposed. ECFT extracted a low-complexity edge feature based on two-level filtering for object representation. For reducing model drift as much as possible, an object model is updated adaptively by the maximum response value of correlation filters. The comparative experiments of 7 trackers on 20 challenging sequences showed that the ECFT tracker can perform better than other trackers in terms of AUC and Precision.

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Metadata
Title
Correlation Filters Tracker Based on Two-Level Filtering Edge Feature
Authors
Dengzhuo Zhang
Donglan Cai
Yun Gao
Hao Zhou
Tianwen Li
Copyright Year
2017
Publisher
Springer Singapore
Book
Computer Vision

Print ISBN: 978-981-10-7298-7

Electronic ISBN: 978-981-10-7299-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-7299-4_44

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