Milan, A.Rezatofighi, H.Dick, A.Reid, I.Schindler, K.2018-12-172018-12-172017Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2017, pp.4225-42322159-53992374-3468http://hdl.handle.net/2440/116855We 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.enCopyright © 2017, Association for the Advancement of Artificial IntelligenceOnline multi-target tracking using recurrent neural networksConference paper00300768170004856307040392-s2.0-85030461126372211Dick, A. [0000-0001-9049-7345]Reid, I. [0000-0001-7790-6423]