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Online Visual Robot Tracking and Identification using Deep LSTM Networks

1,668 views · Published 6 August 2017 · 4:28 · Indexed 5 October 2026

Channel: nimbro · 2017 · Science & Technology

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Video attachement of paper

Hafez Farazi and Sven Behnke:
"Online Visual Robot Tracking and Identification using Deep LSTM Networks"
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada, September 2017. 
http://www.ais.uni-bonn.de/papers/IROS_2017_Farazi.pdf

Collaborative robots working on a common task
are necessary for many applications. One of the challenges for
achieving collaboration in a team of robots is mutual tracking
and identification. We present a novel pipeline for online visionbased
detection, tracking and identification of robots with a
known and identical appearance. Our method runs in realtime
on the limited hardware of the observer robot. Unlike
previous works addressing robot tracking and identification, we
use a data-driven approach based on recurrent neural networks
to learn relations between sequential inputs and outputs. We
formulate the data association problem as multiple classification
problems. A deep LSTM network was trained on a simulated
dataset and fine-tuned on small set of real data. Experiments
on two challenging datasets, one synthetic and one real, which
include long-term occlusions, show promising results.

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