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We propose an RGB-D single-object tracker, built upon the extremely fast RGB-only KCF tracker that is able to exploit depth information to handle scale changes, occlusions, and shape changes. Despite the computational demands of the extra functionalities, we still achieve real-time performance rates of 35–43 fps in MATLAB and 187 fps in our C++ implementation. Our proposed method includes fast depth-based target object segmentation that enables, (1) efficient scale change handling within the KCF core functionality in the Fourier domain, (2) the detection of occlusions by temporal analysis of the target’s depth distribution, and (3) the estimation of a target’s change of shape through the temporal evolution of its segmented silhouette allows. Finally, we provide an in-depth analysis of the factors affecting the throughput and precision of our proposed tracker and perform extensive comparative analysis. Both the MATLAB and C++ versions of our software are available in the public domain.

Original languageEnglish
Pages (from-to)1439-1458
Number of pages20
JournalJournal of Real-Time Image Processing
Early online date24 Nov 2016
Publication statusPublished - 1 Oct 2019

Structured keywords

  • Digital Health


  • Correlation filters
  • Depth-based segmentation
  • Occlusion detection
  • RGB-D tracking
  • Scale and shape changes handling


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