Deep tracking in the wild: End-to-end tracking using recurrent neural networks
This paper presents a novel approach for tracking static and dynamic objects for an autonomous vehicle operating in complex urban environments. Whereas traditional approaches for tracking often feature numerous hand-engineered stages, this method is learned end-to-end and can directly predict a full...
| 主要な著者: | , , , , |
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| フォーマット: | Journal article |
| 出版事項: |
SAGE Publications
2017
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