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, a full end-to-end learning approach for online multitarget
tracking based on deep learning. 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 competitive results obtained at
?300 Hz on a standard CPU, and pave the way towards future
research in this direction.