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• W2950809062 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." @default.
• W2950809062 created "2019-06-27" @default.
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• W2950809062 date "2018-10-11" @default.
• W2950809062 modified "2023-09-23" @default.
• W2950809062 title "Online Visual Robot Tracking and Identification using Deep LSTM Networks" @default.
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