Real-time RGB-D Tracking with Depth Scaling Kernelised Correlation Filters and Occlusion Handling

Massimo Camplani, Sion Hannuna, Majid Mirmehdi, Dima Damen (Aldamen), Lili Tao, Tilo Burghardt, Adeline Paiement

Research output: Contribution to conferenceConference Paperpeer-review

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Abstract

We present a real-time RGB-D object tracker which manages occlusions and scale changes in a wide variety of scenarios. Its accuracy matches, and in many cases outperforms, state-of-the-art algorithms for precision and it far exceeds most in speed. We build our algorithm on the existing colour-only KCF tracker which uses the `kernel trick' to extend correlation filters for fast tracking. We fuse colour and depth cues as the tracker's features, and furthermore, exploit the depth data to both adjust a given target's scale, and detect and manage occlusions in such a way as to maintain real-time performance, exceeding on average 40~fps. We benchmark our approach using 2 publicly available datasets and make our easy-to-extend modularised code available to other researchers.
Original languageEnglish
Pages145.1-145.11
Number of pages11
DOIs
Publication statusPublished - Sep 2015

Structured keywords

  • Digital Health

Keywords

  • RGB-D tracking
  • Kernelised Correlation Filters
  • Depth and Color Fusion
  • Digital Health

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