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- W4298314000 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." @default.
- W4298314000 created "2022-10-02" @default.
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- W4298314000 date "2016-04-12" @default.
- W4298314000 modified "2023-09-27" @default.
- W4298314000 title "Online Multi-Target Tracking Using Recurrent Neural Networks" @default.
- W4298314000 doi "https://doi.org/10.48550/arxiv.1604.03635" @default.
- W4298314000 hasPublicationYear "2016" @default.
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