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RGB-D Tracking Based on Kernelized Correlation Filter with Deep Features

  • Shuang Gu
  • Yao Lu
  • Lin Zhang
  • Jian Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10636)

Abstract

This paper proposes a new RGB-D tracker which is upon Kernelized Correlation Filter(KCF) with deep features. KCF is a high-speed target tracker. However, the HOG feature used in KCF shows some weaknesses, such as not robust to noise. Therefore, we consider using RGB-D deep features in KCF, which refer to deep features of RGB and depth images and the deep features contain abundant and discriminated information for tracking. The mixture of deep features highly improves the performance of the tracker. Besides, KCF is sensitive to scale variations while depth images benefit for handling this problem. According to the principle of similar triangle, the ratio of scale variation can be observed simply. Tested over Princeton RGB-D Tracking Benchmark, Our RGB-D tracker achieves the highest accuracy when no occlusion happens. Meanwhile, we keep the high-speed tracking even if deep features are calculated during tracking and the average speed is 10 FPS.

Keywords

RGB-D KCF Deep features Scale estimation 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61273273) and by Research Fund for the Doctoral Program of Higher Education of China (No. 20121101110034).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Beijing Laboratory of Intelligent Information Technology, School of Computer ScienceBeijing Institute of TechnologyBeijingChina
  2. 2.Advanced Analytics InstituteUniversity of Technology SydneySydneyAustralia

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