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arXiv · 1810.04941

Online Visual Robot Tracking and Identification using Deep LSTM Networks

Hafez Farazi·Sven Behnke
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Abstract

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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Hafez Farazi, Sven Behnke. 2018-10-11. Online Visual Robot Tracking and Identification using Deep LSTM Networks. https://arxiv.org/abs/1810.04941

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