Multimedia Tools and Applications

, Volume 76, Issue 8, pp 10973–10990 | Cite as

Robust mean shift tracking based on refined appearance model and online update

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Abstract

In this paper, a robust mean shift tracking algorithm based on refined appearance model (RAM) and online update strategy is proposed. The main idea of the proposed algorithm is to construct a more accurate appearance model to improve tracking precision and design an online update strategy to adjust to the appearance variation. At the beginning of the tracking, the simple mean shift tracking algorithm is applied on the first few frames to collect a set of target templates, which contains both foreground and background of the target. During the model construction, simple linear iterative clustering (SLIC) algorithm is exploited to obtain the superpixels of the target templates, and the superpixels are further clustered to distinguish the foreground from background. A weighted vector is then obtained based on the classified foreground from background, which is utilized to modify the kernel histogram appearance model. The following frames are processed based on the mean shift tracking algorithm with the modified appearance model, and the stable tracking results with no occlusion will be selected to update the appearance model. The concrete operation of model update is the same as model construction. Experiment results on some challenging test sequences indicate that the proposed algorithm can well cope with both appearance variation and background change to obtain a robust tracking performance. A further comprehensive experiment on OTB2013 demonstrates that the proposed tracking algorithm outperforms the state-of-the-art works in most cases.

Keywords

Visual tracking Mean shift tracking Appearance model Model update 

Notes

Acknowledgments

This research was supported by National Natural Science Foundation of China (No.61473309 and 61403414) and Natural Science Basic Research Plan in Shaanxi Province of China (No.2016JM6050).

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Information and Navigation CollegeAir Force Engineering UniversityXi’anChina

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