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Jonathon Luiten

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

ICRA Conference 2020 Conference Paper

4D Generic Video Object Proposals

Aljosa Osep
Paul Voigtlaender
Mark Weber
Jonathon Luiten
Bastian Leibe

Many high-level video understanding methods require input in the form of object proposals. Currently, such proposals are predominantly generated with the help of neural networks that were trained for detecting and segmenting a set of known object classes, which limits their applicability to cases where all objects of interest are represented in the training set. We propose an approach that can reliably extract spatio-temporal object proposals for both known and unknown object categories from stereo video. Our 4D Generic Video Tubes (4D-GVT) method combines motion cues, stereo data, and data-driven object instance segmentation in a probabilistic framework to compute a compact set of video-object proposals that precisely localizes object candidates and their contours in 3D space and time.

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IROS Conference 2020 Conference Paper

Single-Shot Panoptic Segmentation

Mark Weber
Jonathon Luiten
Bastian Leibe

We present a novel end-to-end single-shot method that segments countable object instances (things) as well as background regions (stuff) into a non-overlapping panoptic segmentation at almost video frame rate. Current state-of-the-art methods are far from reaching video frame rate and mostly rely on merging instance segmentation with semantic background segmentation, making them impractical to use in many applications such as robotics. Our approach relaxes this requirement by using an object detector but is still able to re-solve inter- and intra-class overlaps to achieve a non-overlapping segmentation. On top of a shared encoder-decoder backbone, we utilize multiple branches for semantic segmentation, object detection, and instance center prediction. Finally, our panoptic head combines all outputs into a panoptic segmentation and can even handle conflicting predictions between branches as well as certain false predictions. Our network achieves 32. 6% PQ on MS-COCO at 23. 5 FPS, opening up panoptic segmentation to a broader field of applications.

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ICRA Conference 2019 Conference Paper

Large-Scale Object Mining for Object Discovery from Unlabeled Video

Aljosa Osep
Paul Voigtlaender
Jonathon Luiten
Stefan Breuers
Bastian Leibe

This paper addresses the problem of object discovery from unlabeled driving videos captured in a realistic automotive setting. Identifying recurring object categories in such raw video streams is a very challenging problem. Not only do object candidates first have to be localized in the input images, but many interesting object categories occur relatively infrequently. Object discovery will therefore have to deal with the difficulties of operating in the long tail of the object distribution. We demonstrate the feasibility of performing fully automatic object discovery in such a setting by mining object tracks using a generic object tracker. In order to facilitate further research in objet discovery, we release a collection of more than 360, 000 automatically mined object tracks from 10 + hours of video data (560, 000 frames). We use this dataset to evaluate the suitability of different feature representations and clustering strategies for object discovery.

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