Welcome



Welcome to the Computer Vision Group at RWTH Aachen University!

The Computer Vision group has been established at RWTH Aachen University in context with the Cluster of Excellence "UMIC - Ultra High-Speed Mobile Information and Communication" and is associated with the Chair Computer Sciences 8 - Computer Graphics, Computer Vision, and Multimedia. The group focuses on computer vision applications for mobile devices and robotic or automotive platforms. Our main research areas are visual object recognition, tracking, self-localization, 3D reconstruction, and in particular combinations between those topics.

We offer lectures and seminars about computer vision and machine learning.

You can browse through all our publications and the projects we are working on.

We have one paper accepted at ACCV 2018

Our paper: "PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation" has been accepted for publication at ACCV 2018.

Oct. 5, 2018

We have one paper accepted at the ECCV'18, GMDL Workshop.

Oct. 4, 2018

We won the 1st Large-scale Video Object Segmentation Challenge at ECCV 2018

You can find details on our approach in our short paper.

Sept. 1, 2018

We won the 2018 ECCV PoseTrack Challenge on 3D human pose estimation.

You can find details on our approach in our short paper.

Sept. 1, 2018

We won the DAVIS Challenge on Video Object Segmentation at CVPR 2018

You can find details on our approach in our short paper.

June 18, 2018

We have one paper accepted at the IEEE International Conference on Computer Vision (ICCV) 2017, 3DRMS Workshop.

Sept. 18, 2017

Recent Publications

MOTS: Multi-Object Tracking and Segmentation

arXiv

This paper extends the popular task of multi-object tracking to multi-object tracking and segmentation (MOTS). Towards this goal, we create dense pixel-level annotations for two existing tracking datasets using a semi-automatic annotation procedure. Our new annotations comprise 70,430 pixel masks for 1,084 distinct objects (cars and pedestrians) in 10,870 video frames. For evaluation, we extend existing multi-object tracking metrics to this new task. Moreover, we propose a new baseline method which jointly addresses detection, tracking, and segmentation with a single convolutional network. We demonstrate the value of our datasets by achieving improvements in performance when training on MOTS annotations. We believe that our datasets, metrics and baseline will become a valuable resource towards developing multi-object tracking approaches that go beyond 2D bounding boxes.

 

4D Generic Video Object Proposals

Arxiv:1901.09260

Many high-level video understanding methods require input in the form of object proposals. Currently, such proposals are predominantly generated with the help of 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. This is a restriction for automotive scenarios, where unknown objects can frequently occur. 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 leverages motion cues, stereo data, and object instance segmentation to compute a compact set of video-object proposals that precisely localizes object candidates and their contours in 3D space and time. We show that given only a small amount of labeled data, our 4D-GVT proposal generator generalizes well to real-world scenarios, in which unknown categories appear. It outperforms other approaches that try to detect as many objects as possible by increasing the number of classes in the training set to several thousand.

 

Large-Scale Object Mining for Object Discovery from Unlabeled Video

Accepted to ICRA'19 (to appear)

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 object discovery, we will 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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