Detecting Foreground Disambiguation of Depth Images using Fuzzy Logic

Tanvi Banerjee, James M. Keller, Marjorie Skubic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a unique occlusion and foreground overlap detection technique from depth sensor data using a fuzzy rule-based system. Features such as bounding box parameters and skeletonization were extracted from the foreground images and then input to the Fuzzy Inference System. Overlap and occlusion confidence measures were taken for each frame in the image sequence and compared against the extracted ground truth. This technique can help filter out occluded regions in the image sequence which, in an Eldercare environment, can then be used to compute accurate estimates of fall risk parameters such as stride time, stride length, and walking speed on a daily basis in in order to monitor the well-being of older adults in an ambient assisted living facility.

Original languageAmerican English
Title of host publicationFUZZ-IEEE 2013 - 2013 IEEE International Conference on Fuzzy Systems
PublisherIEEE
ISBN (Electronic)978-1-4799-0022-0, 978-1-4799-0020-6
DOIs
StatePublished - Oct 7 2013
Event2013 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2013 - Hyderabad, India
Duration: Jul 7 2013Jul 10 2013

Conference

Conference2013 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2013
Country/TerritoryIndia
CityHyderabad
Period7/7/137/10/13

ASJC Scopus Subject Areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

Keywords

  • Activity analysis
  • Depth image
  • Fuzzy rules
  • Machine learning
  • Occlusion

Disciplines

  • Bioinformatics
  • Communication Technology and New Media
  • Databases and Information Systems
  • OS and Networks
  • Science and Technology Studies

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