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Multi-object tracking of swarms with active target avoidance

conference contribution
posted on 2018-01-01, 00:00 authored by Y Hettiarachchige, A Khan, Jan Carlo BarcaJan Carlo Barca
Multi-object tracking is a difficult vision task that is necessary for most real world vision applications. This task becomes almost indomitable if the objects collectively act to avoid being tracked. In this paper we present a solution strategy by utilising of two novel trackers, one based on a recurrent deep neural network and the other a one shot associative memory. We solve the problem at its highest difficulty level by incorporating a phenomenon seen in nature called the confusion effect used by swarming animals to evade predators. This behaviour has evolved to actively disrupt the predator's ability to accurately track targets, which makes it an extremely challenging testbed for computer vision. We use our findings to propose a strategy that takes advantage of both the robust tracking accuracy of recurrent neural networks and the rapid training of the one shot associative memory to predict the swarm's next moves.

History

Event

IEEE Control Systems Society. Conference (15th : 2018 : Singapore)

Series

IEEE Control Systems Society Conference

Pagination

1204 - 1209

Publisher

Institute of Electrical and Electronics Engineers

Location

Singapore

Place of publication

Pisctaway, N.J.

Start date

2018-11-18

End date

2018-11-21

ISBN-13

9781538695821

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2018, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

ICARCV 2018 : Proceedings of the 2018 15th International Conference on Control, Automation, Robotics and Vision

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