Realtime Multi-Diver Tracking and Re-identification for Underwater Human-Robot Collaboration

Karin De Langis, Junaed Sattar

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

2 Scopus citations

Abstract

Autonomous underwater robots working with teams of human divers may need to distinguish between different divers, e.g., to recognize a lead diver or to follow a specific team member. This paper describes a technique that enables autonomous underwater robots to track divers in real time as well as to reidentify them. The approach is an extension of Simple Online Realtime Tracking (SORT) with an appearance metric (deep SORT). Initial diver detection is performed with a custom CNN designed for realtime diver detection, and appearance features are subsequently extracted for each detected diver. Next, realtime tracking-by-detection is performed with an extension of the deep SORT algorithm. We evaluate this technique on a series of videos of divers performing human-robot collaborative tasks and show that our methods result in more divers being accurately identified during tracking. We also discuss the practical considerations of applying multi-person tracking to on-board autonomous robot operations, and we consider how failure cases can be addressed during on-board tracking.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages11140-11146
Number of pages7
ISBN (Electronic)9781728173955
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France
Duration: May 31 2020Aug 31 2020

Publication series

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

Conference

Conference2020 IEEE International Conference on Robotics and Automation, ICRA 2020
Country/TerritoryFrance
CityParis
Period5/31/208/31/20

Bibliographical note

Funding Information:
The authors are with the Department of Computer Science and Engineering, Minnesota Robotics Institute, University of Minnesota Twin Cities, Minneapolis, MN, USA. {1dento019, 2junaed} at umn.edu. This work was supported by the National Science Foundation award IIS-1845364.

Publisher Copyright:
© 2020 IEEE.

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