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Type: Conference paper
Title: Online multi-target tracking using recurrent neural networks
Author: Milan, A.
Rezatofighi, H.
Dick, A.
Reid, I.
Schindler, K.
Citation: Proceedings of 31st AAAI Conference on Artificial Intelligence and the 29th Innovative Applications of Artificial Intelligence Conference, 2017 / pp.4225-4232
Issue Date: 2017
ISSN: 2159-5399
Conference Name: 31st AAAI Conference on Artificial Intelligence (AAAI 2017) (04 Feb 2017 - 09 Feb 2017 : San Francisco)
Statement of
Anton Milan, S. Hamid Rezatofighi, Anthony Dick, Ian Reid, Konrad Schindler
Abstract: We present a novel approach to online multi-target tracking based on recurrent neural networks (RNNs). Tracking multiple objects in real-world scenes involves many challenges, including a) an a-priori unknown and time-varying number of targets, b) a continuous state estimation of all present targets, and c) a discrete combinatorial problem of data association. Most previous methods involve complex models that require tedious tuning of parameters. Here, we propose for the first time, an end-to-end learning approach for online multi-target tracking. Existing deep learning methods are not designed for the above challenges and cannot be trivially applied to the task. Our solution addresses all of the above points in a principled way. Experiments on both synthetic and real data show promising results obtained at ~300 Hz on a standard CPU, and pave the way towards future research in this direction.
Rights: Copyright © 2017, Association for the Advancement of Artificial Intelligence
RMID: 0030076817
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Appears in Collections:Australian Institute for Machine Learning publications
Computer Science publications

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