Joel Janai

Ph.D. Student

Autonomous Vision

Perceiving Systems

Office: S2.017
Max-Planck-Ring 4
72076 Tübingen
Germany

+49 7071 601 1855

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Advisor(s):
Andreas Geiger

Biography
Publications

Education

since Jul 2015	Doctor of Philosophy (Ph.D.) in Computer Vision
	Max-Planck-Institute for Intelligent Systems, Tübingen, Germany Perceiving Systems Department Supervisor: Andreas Geiger

Sep 2012 - May 2015	Master of Science in Computer Science
	University of Bonn, Germany Emphasis: Intelligent Systems Thesis: "Tracking of Arbitrary Objects in 3D Laser Range Data"

Sep 2009 - Sep 2012	Bachelor of Science in Computer Science
	University of Bonn, Germany Thesis: "Person Detection in 3D Laser Range Data"

Experience

Jun 2018 - Sep 2018	Research Intern - Computational Photography at Facebook, Seattle, US
Apr 2014 - Dec 2014	Student Assistant at AIS University Bonn, Germany
Oct 2013 - Mar 2014	Research Intern - Data Mining at Robert Bosch LLC, Palo Alto, USA
Apr 2013 - Sep 2013	Teaching Assistant at IVS University Bonn, Germany
May 2012 - Mar 2013	Student Assistant at IVS University Bonn, Germany

3 results (View BibTeX file of all listed publications)

2018

Unsupervised Learning of Multi-Frame Optical Flow with Occlusions

Janai, J., Güney, F., Ranjan, A., Black, M. J., Geiger, A.

In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11220, pages: 713-731, Springer, Cham, September 2018 (inproceedings)

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pdf suppmat Video Project Page DOI Project Page [BibTex]

2018

avg ps Janai, J., Güney, F., Ranjan, A., Black, M. J., Geiger, A. Unsupervised Learning of Multi-Frame Optical Flow with Occlusions In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11220, pages: 713-731, Springer, Cham, September 2018 (inproceedings)

pdf suppmat Video Project Page DOI Project Page [BibTex]

2017

Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data

Janai, J., Güney, F., Wulff, J., Black, M., Geiger, A.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pages: 1406-1416, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract

Existing optical flow datasets are limited in size and variability due to the difficulty of capturing dense ground truth. In this paper, we tackle this problem by tracking pixels through densely sampled space-time volumes recorded with a high-speed video camera. Our model exploits the linearity of small motions and reasons about occlusions from multiple frames. Using our technique, we are able to establish accurate reference flow fields outside the laboratory in natural environments. Besides, we show how our predictions can be used to augment the input images with realistic motion blur. We demonstrate the quality of the produced flow fields on synthetic and real-world datasets. Finally, we collect a novel challenging optical flow dataset by applying our technique on data from a high-speed camera and analyze the performance of the state-of-the-art in optical flow under various levels of motion blur.

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pdf suppmat Project page Video DOI Project Page [BibTex]

2017

avg ps Janai, J., Güney, F., Wulff, J., Black, M., Geiger, A. Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pages: 1406-1416, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

pdf suppmat Project page Video DOI Project Page [BibTex]

Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

Janai, J., Güney, F., Behl, A., Geiger, A.

Arxiv, 2017 (article)

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.

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pdf Project Page Project Page [BibTex]

avg Janai, J., Güney, F., Behl, A., Geiger, A. Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art Arxiv, 2017 (article)

pdf Project Page Project Page [BibTex]

Joel Janai

Education

Experience

2018

Unsupervised Learning of Multi-Frame Optical Flow with Occlusions

2018

2017

Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data

2017

Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

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