Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/116855
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 the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2017, pp.4225-4232 |
Publisher: | ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE |
Issue Date: | 2017 |
Series/Report no.: | AAAI Conference on Artificial Intelligence |
ISSN: | 2159-5399 2374-3468 |
Conference Name: | 31st AAAI Conference on Artificial Intelligence (AAAI 2017) (4 Feb 2017 - 9 Feb 2017 : San Francisco) |
Statement of Responsibility: | 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 |
Grant ID: | http://purl.org/au-research/grants/arc/LP130100154 http://purl.org/au-research/grants/arc/FL130100102 http://purl.org/au-research/grants/arc/CE140100016 |
Published version: | https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14184 |
Appears in Collections: | Aurora harvest 3 Australian Institute for Machine Learning publications Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.