Comparative evaluation of methods for filtering Kinect depth data

Kyis Essmaeel, Luigi Gallo, Ernesto Damiani, Giuseppe De Pietro, Albert Dipanda

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

The release of the Kinect has fostered the design of novel methods and techniques in several application domains. It has been tested in different contexts, which span from home entertainment to surgical environments. Nonetheless, to promote its adoption to solve real-world problems, the Kinect should be evaluated in terms of precision and accuracy. Up to now, some filtering approaches have been proposed to enhance the precision and accuracy of the Kinect sensor, and preliminary studies have shown promising results. In this work, we discuss the results of a study in which we have compared the most commonly used filtering approaches for Kinect depth data, in both static and dynamic contexts, by using novel metrics. The experimental results show that each approach can be profitably used to enhance the precision and/or accuracy of Kinect depth data in a specific context, whereas the temporal filtering approach is able to reduce noise in different experimental conditions.

Original languageBritish English
Pages (from-to)7331-7354
Number of pages24
JournalMultimedia Tools and Applications
Volume74
Issue number17
DOIs
StatePublished - 1 Sep 2015

Keywords

  • Bilateral filter
  • Comparative evaluation
  • Depth data
  • Depth instability
  • Kinect
  • Median filter
  • Temporal denoising

Fingerprint

Dive into the research topics of 'Comparative evaluation of methods for filtering Kinect depth data'. Together they form a unique fingerprint.

Cite this