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 are excited to share that our paper HOTA: A Higher Order Metric for Evaluating Multi-object Tracking has been accepted for publication in the International Journal of Computer Vision (IJCV'20).

Nov. 3, 2020


We have one paper accepted at the 2021 Winter Conference on Applications of Computer Vision (WACV ’21)

Nov. 2, 2020

We won the ECCV2020 "3D Poses in the Wild" Challenge!

See our MeTRAbs paper, accepted for publication in the IEEE T-BIOM special journal issue "Selected Best works on Automatic Face and Gesture Recognition 2020" for our approach and check out the code on GitHub.

Aug. 23, 2020


We have one paper accepted at the British Machine Vision Conference (BMVC) 2020:

July 29, 2020


Excited to share that our paper Single-Shot Panoptic Segmentation has been accepted to IROS'20.

July 1, 2020


We have two papers accepted at the European Conference on Computer Vision (ECCV) 2020:

June 24, 2020

Recent Publications

MeTRAbs: Metric-Scale Truncation-Robust Heatmaps for Absolute 3D Human Pose Estimation

IEEE Transactions on Biometrics, Behavior, and Identity Science (T-BIOM), Selected Best Works From Automatic Face and Gesture Recognition 2020 (to appear)

Heatmap representations have formed the basis of human pose estimation systems for many years, and their extension to 3D has been a fruitful line of recent research. This includes 2.5D volumetric heatmaps, whose X and Y axes correspond to image space and Z to metric depth around the subject. To obtain metric-scale predictions, 2.5D methods need a separate post-processing step to resolve scale ambiguity. Further, they cannot localize body joints outside the image boundaries, leading to incomplete estimates for truncated images. To address these limitations, we propose metric-scale truncation-robust (MeTRo) volumetric heatmaps, whose dimensions are all defined in metric 3D space, instead of being aligned with image space. This reinterpretation of heatmap dimensions allows us to directly estimate complete, metric-scale poses without test-time knowledge of distance or relying on anthropometric heuristics, such as bone lengths. To further demonstrate the utility our representation, we present a differentiable combination of our 3D metric-scale heatmaps with 2D image-space ones to estimate absolute 3D pose (our MeTRAbs architecture). We find that supervision via absolute pose loss is crucial for accurate non-root-relative localization. Using a ResNet-50 backbone without further learned layers, we obtain state-of-the-art results on Human3.6M, MPI-INF-3DHP and MuPoTS-3D. Our code is publicly available to facilitate further research.

Reducing the Annotation Effort for Video Object Segmentation Datasets

2021 Winter Conference on Applications of Computer Vision (WACV ’21)

For further progress in video object segmentation (VOS), larger, more diverse, and more challenging datasets will be necessary. However, densely labeling every frame with pixel masks does not scale to large datasets. We use a deep convolutional network to automatically create pseudo-labels on a pixel level from much cheaper bounding box annotations and investigate how far such pseudo-labels can carry us for training state-of-the-art VOS approaches. A very encouraging result of our study is that adding a manually annotated mask in only a single video frame for each object is sufficient to generate pseudo-labels which can be used to train a VOS method to reach almost the same performance level as when training with fully segmented videos. We use this workflow to create pixel pseudo-labels for the training set of the challenging tracking dataset TAO, and we manually annotate a subset of the validation set. Together, we obtain the new TAO-VOS benchmark, which we make publicly available at http://www.vision.rwth-aachen.de/page/taovos. While the performance of state-of-the-art methods on existing datasets starts to saturate, TAO-VOS remains very challenging for current algorithms and reveals their shortcomings.

HOTA: A Higher Order Metric for Evaluating Multi-object Tracking

International Journal of Computer Vision (IJCV) 2020

Multi-object tracking (MOT) has been notoriously difficult to evaluate. Previous metrics overemphasize the importance of either detection or association. To address this, we present a novel MOT evaluation metric, higher order tracking accuracy (HOTA), which explicitly balances the effect of performing accurate detection, association and localization into a single unified metric for comparing trackers. HOTA decomposes into a family of sub-metrics which are able to evaluate each of five basic error types separately, which enables clear analysis of tracking performance. We evaluate the effectiveness of HOTA on the MOTChallenge benchmark, and show that it is able to capture important aspects of MOT performance not previously taken into account by established metrics. Furthermore, we show HOTA scores better align with human visual evaluation of tracking performance.

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