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.


Semantic Segmentation dataset released

We just uploaded our dataset used to train the semantic classifier in our ICRA 2016 paper on tracking of generic objects. You can find the dataset here.

May 23, 2016

New Website

The new Website has been launched. The content from the old website will be gradually added to the new site.

April 11, 2016

Recent Publications

Probabilistic Labeling Cost for High-Accuracy Multi-view Reconstruction

IEEE Conference on Computer Vision and Pattern Recognition (CVPR'14)

In this paper, we propose a novel labeling cost for multiview reconstruction. Existing approaches use data terms with specific weaknesses that are vulnerable to common challenges, such as low-textured regions or specularities. Our new probabilistic method implicitly discards outliers and can be shown to become more exact the closer we get to the true object surface. Our approach achieves top results among all published methods on the Middlebury DINO SPARSE dataset and also delivers accurate results on several other datasets with widely varying challenges, for which it works in unchanged form.


Tracking People and Their Objects

IEEE Conference on Computer Vision and Pattern Recognition (CVPR'13)

Current pedestrian tracking approaches ignore impor- tant aspects of human behavior. Humans are not moving independently, but they closely interact with their environ- ment, which includes not only other persons, but also dif- ferent scene objects. Typical everyday scenarios include people moving in groups, pushing child strollers, or pulling luggage. In this paper, we propose a probabilistic approach for classifying such person-object interactions, associating objects to persons, and predicting how the interaction will most likely continue. Our approach relies on stereo depth information in order to track all scene objects in 3D, while simultaneously building up their 3D shape models. These models and their relative spatial arrangement are then fed into a probabilistic graphical model which jointly infers pairwise interactions and object classes. The inferred inter- actions can then be used to support tracking by recovering lost object tracks. We evaluate our approach on a novel dataset containing more than 15,000 frames of person- object interactions in 325 video sequences and demonstrate good performance in challenging real-world scenarios.


Taking Mobile Multi-Object Tracking to the Next Level: People, Unknown Objects, and Carried Items

European Conference on Computer Vision (ECCV'12)

In this paper, we aim to take mobile multi-object tracking to the next level. Current approaches work in a tracking-by-detection framework, which limits them to object categories for which pre-trained detector models are available. In contrast, we propose a novel tracking-before-detection approach that can track both known and unknown object categories in very challenging street scenes. Our approach relies on noisy stereo depth data in order to segment and track objects in 3D. At its core is a novel, compact 3D representation that allows us to robustly track a large variety of objects, while building up models of their 3D shape online. In addition to improving tracking performance, this represensation allows us to detect anomalous shapes, such as carried items on a person’s body. We evaluate our approach on several challenging video sequences of busy pedestrian zones and show that it outperforms state-of-the-art approaches.

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