@inproceedings{HubertDollSick2024, author = {Hubert, Andreas and Doll, Konrad and Sick, Bernhard}, title = {Influence of Background Color on 6D Pose Tracking Accuracy}, series = {2024 International Conference on Engineering and Emerging Technologies (ICEET), 27-28 December 2024}, booktitle = {2024 International Conference on Engineering and Emerging Technologies (ICEET), 27-28 December 2024}, publisher = {IEEE}, doi = {10.1109/ICEET65156.2024.10913824}, pages = {1 -- 6}, year = {2024}, subject = {Maschinelles Lernen}, language = {en} } @incollection{Hubert2024, author = {Hubert, Andreas}, title = {Scene Understanding at Manual Assembly Cells}, series = {Organic Computing}, volume = {2023}, booktitle = {Organic Computing}, publisher = {Kassel University Press}, address = {Kassel}, pages = {109 -- 120}, year = {2024}, subject = {Maschinelles Lernen}, language = {en} } @inproceedings{HubertJungDoll2023, author = {Hubert, Andreas and Jung, Janis and Doll, Konrad}, title = {Exploiting Self-Imposed Constraints on RGB and LiDAR for Unsupervised Training}, series = {Proceedings of the 2023 6th International Conference on Machine Vision and Applications}, booktitle = {Proceedings of the 2023 6th International Conference on Machine Vision and Applications}, publisher = {ACM}, address = {New York, NY, USA}, doi = {https://doi.org/10.1145/3589572.3589575}, pages = {15 -- 21}, year = {2023}, abstract = {Hand detection on single images is an intensively researched area, and reasonable solutions are already available today. However, fine-tuning detectors within a specific domain remains a tedious task. Unsupervised training procedures can reduce the effort required to create domain-specific datasets and models. In addition, different modalities of the same physical space, here color and depth data, represent objects differently and thus allow for exploitation. We introduce and evaluate a training pipeline to exploit the modalities in an unsupervised manner. The supervision is omitted by choosing suitable self-imposed constraints for the data source. We compare our training results with ground truth training results and show that with these modalities, the domain can be extended without a single annotation, e.g., for detecting colored gloves.}, subject = {Maschinelles Sehen}, language = {en} } @inproceedings{GoldhammerHubertKoehleretal.2014, author = {Goldhammer, Michael and Hubert, Andreas and K{\"o}hler, Sebastian and Zindler, Klaus and Brunsmann, Ulrich and Doll, Konrad and Sick, Bernhard}, title = {Analysis on Termination of Pedestrians' Gait at Urban Intersections}, series = {Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on}, booktitle = {Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on}, publisher = {IEEE}, address = {Qingdao, China}, doi = {10.1109/ITSC.2014.6957947}, pages = {1758 -- 1763}, year = {2014}, subject = {Fahrerassistenzsystem}, language = {en} } @incollection{LampertJungHubertetal.2022, author = {Lampert, Pascal and Jung, Janis and Hubert, Andreas and Doll, Konrad}, title = {Looping Through Color Space: A Simple Augmentation Method to Improve Biased Object Detection}, series = {Lecture Notes in Networks and Systems}, booktitle = {Lecture Notes in Networks and Systems}, publisher = {Springer Nature Singapore}, address = {Singapore}, isbn = {9789811916069}, issn = {2367-3370}, doi = {10.1007/978-981-19-1607-6_61}, pages = {687 -- 698}, year = {2022}, subject = {Objekterkennung}, language = {en} } @inproceedings{MittelHubertDingetal.2023, author = {Mittel, Dominik and Hubert, Andreas and Ding, Junsheng and Perzylo, Alexander}, title = {Towards a Knowledge-Augmented Socio-Technical Assistance System for Product Engineering}, series = {2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA)}, booktitle = {2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA)}, publisher = {IEEE}, doi = {10.1109/ETFA54631.2023.10275386}, pages = {1 -- 4}, year = {2023}, abstract = {Digital tools for handling the whole product engineering phase are getting more and more important in the context of Industry 4.0 and an increasing product variety. However, especially in small and medium-sized enterprises, a lot of information about product development and production is stored in different documents or isolated data silos. A promising way to arrive at a solution is to model data and knowledge with ontologies and enrich it with context information. This paper presents a concept and a showcase implementation of a company-internal and personalized assistance system for an end-to-end digital product engineering process. We combine a generic and cost-efficient human assistance solution focusing on social aspects and a company-wide knowledge graph to create a seamless and highly integrated data structure that assists many stakeholders in the product engineering process, from product designers to assembly workers. As a result, more complex products can be handled and the product engineering process can be accelerated.}, subject = {Produktentwicklung}, language = {en} } @inproceedings{ReichertHetzelHubertetal.2024, author = {Reichert, Hannes and Hetzel, Manuel and Hubert, Andreas and Doll, Konrad and Sick, Bernhard}, title = {Sensor Equivariance: A Framework for Semantic Segmentation with Diverse Camera Models}, series = {2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, booktitle = {2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/CVPRW63382.2024.00132}, pages = {1254 -- 1261}, year = {2024}, subject = {Bildverarbeitung}, language = {en} } @article{BieshaarZernetschHubertetal.2018, author = {Bieshaar, Maarten and Zernetsch, Stefan and Hubert, Andreas and Sick, Bernhard and Doll, Konrad}, title = {Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble}, series = {IEEE Transactions on Intelligent Vehicles}, volume = {3}, journal = {IEEE Transactions on Intelligent Vehicles}, number = {4}, pages = {534 -- 544}, year = {2018}, abstract = {In the future, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation on different levels, such as situation prediction or intention detection. In this paper, we present a cooperative approach for starting movement detection of cyclists using a boosted stacking ensemble approach realizing feature- and decision-level cooperation. We introduce a novel method based on a three-dimensional convolutional neural network (CNN) to detect starting motions on image sequences by learning spatio-temporal features. The CNN is complemented by a smart device based starting movement detection originating from smart devices carried by the cyclist. Both model outputs are combined in a stacking ensemble approach using an extreme gradient boosting classifier resulting in a fast and yet robust cooperative starting movement detector. We evaluate our cooperative approach on real-world data originating from experiments with 49 test subjects consisting of 84 starting motions.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{HubertZernetschDolletal.2017, author = {Hubert, Andreas and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Cyclists starting behavior at intersections}, series = {2017 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2017 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Los Angeles, CA, USA}, doi = {10.1109/IVS.2017.7995856}, pages = {1071 -- 1077}, year = {2017}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{JungHubertDolletal.2022, author = {Jung, Janis and Hubert, Andreas and Doll, Konrad and Kr{\"o}hn, Michael and Stadler, Jochen}, title = {Prozessinnovation}, series = {Wissenstransfer im Spannungsfeld von Autonomisierung und Fachkr{\"a}ftemangel, Tagungsband, 18. AALE-Konferenz, Pforzheim, 09.03.-11.03.2022}, booktitle = {Wissenstransfer im Spannungsfeld von Autonomisierung und Fachkr{\"a}ftemangel, Tagungsband, 18. AALE-Konferenz, Pforzheim, 09.03.-11.03.2022}, publisher = {Hochschule f{\"u}r Technik, Wirtschaft und Kultur Leipzig}, doi = {10.33968/2022.20}, year = {2022}, abstract = {Manuelle Montageprozesse sind nach wie vor unverzichtbar in vielen Bereichen der produzierenden Industrie. Vor allem die Qualit{\"a}tskontrolle, sowie das Einlernen neuer Mitarbeitenden stellen Betriebe durch die voranschreitende Digitalisierung vor neue Herausforderungen. Assistenzsysteme k{\"o}nnen hier helfen, die L{\"u}cke zwischen Anforderungen und Qualifikation zu {\"u}berbr{\"u}cken. Wir stellen einen Ansatz zur intelligenten Assistenz vor, welcher auf einer kamerabasierten Erkennung von Arbeitsabl{\"a}ufen mit Hilfe von Methoden des maschinellen Lernens beruht. Das Assistenzsystem erzeugt automatisiert Hilfsmaterial zur Unterst{\"u}tzung der Werkenden. Zus{\"a}tzlich zur Darstellung der technischen Aspekte, werden psychologische Aspekte, wie Akzeptanz und Motivation untersucht.}, subject = {Assistenzsystem}, language = {de} } @inproceedings{HubertGuoDolletal.2025, author = {Hubert, Andreas and Guo, Chenxi and Doll, Konrad and Sick, Bernhard}, title = {Improving Fast 6D Pose Estimation with Adaptive Model Fusion}, series = {2025 5th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)}, booktitle = {2025 5th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/MLISE66443.2025.11100239}, pages = {58 -- 63}, year = {2025}, subject = {Maschinelles Lernen}, language = {en} }