Mixed-domain biological motion tracking for underwater human-robot interaction

Md Jahidul Islam, Junaed Sattar

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

8 Scopus citations

Abstract

We present a novel algorithm for an autonomous underwater robot to visually detect and follow its companion human diver. Using both spatial-domain and frequency-domain features pertaining to human swimming patterns, we devise an algorithm to visually detect the position and swimming direction of the diver. Our algorithm is unique in the way that it allows detection of arbitrary motion directions, in addition to keeping track of a diver's position through the image sequence over time. A Hidden Markov Model (HMM)-based approach prunes the search-space of all potential trajectories relying on image intensities in the spatial-domain. A diver's motion signature is subsequently detected in a sequence of non-overlapping image subwindows exhibiting human swimming patterns. The pruning step ensures efficient computation by avoiding exponentially large search-spaces, whereas the frequency-domain detection allows us to detect the diver's position and motion direction accurately. Experimental validation of the proposed approach is presented on datasets collected from open-water and closed-water environments.

Original languageEnglish (US)
Title of host publicationICRA 2017 - IEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4457-4464
Number of pages8
ISBN (Electronic)9781509046331
DOIs
StatePublished - Jul 21 2017
Event2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore
Duration: May 29 2017Jun 3 2017

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Other

Other2017 IEEE International Conference on Robotics and Automation, ICRA 2017
Country/TerritorySingapore
CitySingapore
Period5/29/176/3/17

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