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An RGBD Tracker Based on KCF Adaptively Handling Long-Term Occlusion

  • Xue-Fei Zhang
  • Ai-Ping Zeng
  • Shan Huang
  • Ming Qing
  • Yi ZhouEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

Abstract

Since occlusion still be a challenge for object tracking in RGB data. In this paper, we propose an RGBD single-object tracker that built upon the well-known base KCF tracker and exploit how the depth information fusing to handle partial and long-term occlusion. To divides tracking model into parts, the proposed tracker could detect and handle occlusion of each part separately. Despite the robustness in tracking with long-term occlusion, our part-based tracker provides an adaptively updating learning matrix. Experimental results are conducted on our dataset, which demonstrate that our tracker contains stability in long-term tracking.

Keywords

RGBD tracking Kernel Long-term occlusion RealSense 

Notes

Acknowledgment

At the point of finishing this paper, we are grateful for the support by the ChaoYing Technology, Co, Ltd, Sichuan, China.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Xue-Fei Zhang
    • 1
    • 2
  • Ai-Ping Zeng
    • 2
  • Shan Huang
    • 2
  • Ming Qing
    • 1
  • Yi Zhou
    • 1
    Email author
  1. 1.Chaoying Technology, Co., Ltd.ChengduChina
  2. 2.College of Electrical Engineering and Information TechnologySichuan UniversityChengduChina

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