Object Tracking Based on Mean Shift Algorithm and Kernelized Correlation Filter Algorithm

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10636)

Abstract

In order to solve the problems of motion blur and fast motion, a new robust object tracking algorithm using the Kernelized Correlation Filters (KCF) and the Mean Shift (MS) algorithm, called KCFMS is presented in this paper. The object tracking process can be described as: First, we give the initial position and size of the object and use the Mean Shift algorithm to obtain the position of the object. Second, the Kernelized Correlation Filtering algorithm is used to obtain the position of the object in the same frame. Third, we use the cross update strategy to update the object models. In order to improve the tracking speed as much as possible, our object tracking algorithm works only over one layer. This hybrid algorithm has a good tracking effect on the target fast motion and motion blur. We present extensive experimental results on a number of challenging sequences in terms of efficiency, accuracy and robustness.

Keywords

Kernelized Correlation Filters Mean shift Motion blur Fast motion 

Notes

Acknowledgment

This work is partially supported by the National Natural Science Foundation of China (61402310). Natural Science Foundation of Jiangsu Province of China (BK20141195).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjingChina

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