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 Paper

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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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  • Projects

    SPHERE (EPSRC IRC)

    Craddock, I. J., Coyle, D. T., Flach, P. A., Kaleshi, D., Mirmehdi, M., Piechocki, R. J., Stark, B. H., Ascione, R., Ashburn, A. M., Burnett, M. E., Aldamen, D., Gooberman-Hill, R. J. S., Harwin, W. S., Hilton, G., Holderbaum, W., Holley, A. P., Manchester, V. A., Meller, B. J., Stack, E. & Gilchrist, I. D.

    1/10/1330/09/18

    Project: Research, Parent

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