Аннотация
Recent years have witnessed amazing progress in AI related fields such as
computer vision, machine learning and autonomous vehicles. As with any rapidly
growing field, however, it becomes increasingly difficult to stay up-to-date or
enter the field as a beginner. While several topic specific survey papers have
been written, to date no general survey on problems, datasets and methods in
computer vision for autonomous vehicles exists. This paper attempts to narrow
this gap by providing a state-of-the-art survey on this topic. Our survey
includes both the historically most relevant literature as well as the current
state-of-the-art on several specific topics, including recognition,
reconstruction, motion estimation, tracking, scene understanding and end-to-end
learning. Towards this goal, we first provide a taxonomy to classify each
approach and then analyze the performance of the state-of-the-art on several
challenging benchmarking datasets including KITTI, ISPRS, MOT and Cityscapes.
Besides, we discuss open problems and current research challenges. To ease
accessibility and accommodate missing references, we will also provide an
interactive platform which allows to navigate topics and methods, and provides
additional information and project links for each paper.
Описание
Computer Vision for Autonomous Vehicles: Problems, Datasets and
State-of-the-Art
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