Deep tracking in the wild: End-to-end tracking using recurrent neural networks

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...

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Bibliografiska uppgifter
Huvudupphovsmän:	Dequaire, J, Ondrúška, P, Rao, D, Wang, D, Posner, H
Materialtyp:	Journal article
Publicerad:	SAGE Publications 2017

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