AAAI Publications, Thirty-First AAAI Conference on Artificial Intelligence

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Online Multi-Target Tracking Using Recurrent Neural Networks
Anton Milan, S. Hamid Rezatofighi, Anthony Dick, Ian Reid, Konrad Schindler

Last modified: 2017-02-12


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.


Multi-target tracking; recurrent neural networks; long short-term memory; data association

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