Online Multi-Target Tracking Using Recurrent Neural Networks
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.
Cite
@article{arxiv.1604.03635,
title = {Online Multi-Target Tracking Using Recurrent Neural Networks},
author = {Anton Milan and Seyed Hamid Rezatofighi and Anthony Dick and Ian Reid and Konrad Schindler},
journal= {arXiv preprint arXiv:1604.03635},
year = {2016}
}