Online multi-target tracking using recurrent neural networks
Date
2017
Authors
Milan, A.
Rezatofighi, H.
Dick, A.
Reid, I.
Schindler, K.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2017, pp.4225-4232
Statement of Responsibility
Anton Milan, S. Hamid Rezatofighi, Anthony Dick, Ian Reid, Konrad Schindler
Conference Name
31st AAAI Conference on Artificial Intelligence (AAAI 2017) (4 Feb 2017 - 9 Feb 2017 : San Francisco)
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.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright © 2017, Association for the Advancement of Artificial Intelligence