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2018 | OriginalPaper | Chapter

Learning Temporal Context for Correlation Tracking with Scale Estimation

Authors : Yuhao Cui, Haoqian Wang, Xingzheng Wang, Yi Yang

Published in: Advances in Multimedia Information Processing – PCM 2017

Publisher: Springer International Publishing

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Abstract

Visual object tracking is a fundamental task in computer vision with its wide range of applications. In this paper, we propose a robust algorithm based on the kernelized correlation filter framework to handle occlusions or scale variations. Our algorithm takes into account the relationships between the target object and its surrounding context, and learns a discriminative correlation filter for the estimation of the new position. Another discriminative regression model via constructing the target pyramid is introduced to estimate the optimal scale. The proposed algorithm integrated with two discriminative regression models can track complex targets with occlusion and deformation at real-time. The competitive experimental results on the dataset sequences show that the proposed tracker outperforms other state-of-the-art methods, in both the precision and the success rate.

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Title: Learning Temporal Context for Correlation Tracking with Scale Estimation
Authors: Yuhao Cui
Haoqian Wang
Xingzheng Wang
Yi Yang
Publisher: Springer International Publishing
Book: Advances in Multimedia Information Processing – PCM 2017
Print ISBN: 978-3-319-77379-7

Electronic ISBN: 978-3-319-77380-3

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-77380-3_72

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