%0 Journal Article %1 janai2017computer %A Janai, Joel %A Güney, Fatma %A Behl, Aseem %A Geiger, Andreas %D 2017 %K vision %T Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art %U http://arxiv.org/abs/1704.05519 %X 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.

@article{janai2017computer, abstract = {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.}, added-at = {2017-06-20T14:18:26.000+0200}, author = {Janai, Joel and Güney, Fatma and Behl, Aseem and Geiger, Andreas}, biburl = {https://www.bibsonomy.org/bibtex/283278aac95b0f3a2c85d13636962f9c5/axel.vogler}, description = {Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art}, interhash = {6546363dfcf5c6e115a57ba6ffec2513}, intrahash = {83278aac95b0f3a2c85d13636962f9c5}, keywords = {vision}, note = {cite arxiv:1704.05519}, timestamp = {2017-06-20T14:18:51.000+0200}, title = {Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art}, url = {http://arxiv.org/abs/1704.05519}, year = 2017 }