TY - CHAP A1 - Hubert, Andreas A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Influence of Background Color on 6D Pose Tracking Accuracy T2 - 2024 International Conference on Engineering and Emerging Technologies (ICEET), 27-28 December 2024 KW - Maschinelles Lernen KW - Deep Learning Y1 - 2024 U6 - https://doi.org/10.1109/ICEET65156.2024.10913824 SP - 1 EP - 6 PB - IEEE ER - TY - CHAP A1 - Hubert, Andreas T1 - Scene Understanding at Manual Assembly Cells BT - Doctoral Dissertation Colloquium 2023 T2 - Organic Computing KW - Maschinelles Lernen KW - Montage KW - Simulation Y1 - 2024 VL - 2023 SP - 109 EP - 120 PB - Kassel University Press CY - Kassel ER - TY - CHAP A1 - Hubert, Andreas A1 - Jung, Janis A1 - Doll, Konrad T1 - Exploiting Self-Imposed Constraints on RGB and LiDAR for Unsupervised Training T2 - Proceedings of the 2023 6th International Conference on Machine Vision and Applications N2 - 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. KW - Maschinelles Sehen Y1 - 2023 U6 - https://doi.org/https://doi.org/10.1145/3589572.3589575 SP - 15 EP - 21 PB - ACM CY - New York, NY, USA ER - TY - CHAP A1 - Goldhammer, Michael A1 - Hubert, Andreas A1 - Köhler, Sebastian A1 - Zindler, Klaus A1 - Brunsmann, Ulrich A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Analysis on Termination of Pedestrians‘ Gait at Urban Intersections T2 - Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2014 U6 - https://doi.org/10.1109/ITSC.2014.6957947 SP - 1758 EP - 1763 PB - IEEE CY - Qingdao, China ER - TY - CHAP A1 - Lampert, Pascal A1 - Jung, Janis A1 - Hubert, Andreas A1 - Doll, Konrad T1 - Looping Through Color Space: A Simple Augmentation Method to Improve Biased Object Detection T2 - Lecture Notes in Networks and Systems KW - Objekterkennung Y1 - 2022 SN - 9789811916069 U6 - https://doi.org/10.1007/978-981-19-1607-6_61 SN - 2367-3370 SP - 687 EP - 698 PB - Springer Nature Singapore CY - Singapore ER - TY - CHAP A1 - Mittel, Dominik A1 - Hubert, Andreas A1 - Ding, Junsheng A1 - Perzylo, Alexander T1 - Towards a Knowledge-Augmented Socio-Technical Assistance System for Product Engineering T2 - 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA) N2 - 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. KW - Produktentwicklung KW - Klein- und Mittelbetrieb Y1 - 2023 U6 - https://doi.org/10.1109/ETFA54631.2023.10275386 SP - 1 EP - 4 PB - IEEE ER - TY - CHAP A1 - Reichert, Hannes A1 - Hetzel, Manuel A1 - Hubert, Andreas A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Sensor Equivariance: A Framework for Semantic Segmentation with Diverse Camera Models T2 - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) KW - Bildverarbeitung KW - Sensor Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1109/CVPRW63382.2024.00132 SP - 1254 EP - 1261 PB - IEEE ER - TY - JOUR A1 - Bieshaar, Maarten A1 - Zernetsch, Stefan A1 - Hubert, Andreas A1 - Sick, Bernhard A1 - Doll, Konrad T1 - Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble JF - IEEE Transactions on Intelligent Vehicles N2 - 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. KW - Fahrerassistenzsystem KW - Fahrrad KW - Sensortechnik Y1 - 2018 VL - 3 IS - 4 SP - 534 EP - 544 ER - TY - CHAP A1 - Hubert, Andreas A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Cyclists starting behavior at intersections T2 - 2017 IEEE Intelligent Vehicles Symposium (IV) KW - Fahrerassistenzsystem KW - Radfahrer Y1 - 2017 U6 - https://doi.org/10.1109/IVS.2017.7995856 SP - 1071 EP - 1077 PB - IEEE CY - Los Angeles, CA, USA ER - TY - CHAP A1 - Jung, Janis A1 - Hubert, Andreas A1 - Doll, Konrad A1 - Kröhn, Michael A1 - Stadler, Jochen T1 - Prozessinnovation T2 - Wissenstransfer im Spannungsfeld von Autonomisierung und Fachkräftemangel, Tagungsband, 18. AALE-Konferenz, Pforzheim, 09.03.-11.03.2022 N2 - Manuelle Montageprozesse sind nach wie vor unverzichtbar in vielen Bereichen der produzierenden Industrie. Vor allem die Qualitätskontrolle, sowie das Einlernen neuer Mitarbeitenden stellen Betriebe durch die voranschreitende Digitalisierung vor neue Herausforderungen. Assistenzsysteme können hier helfen, die Lücke zwischen Anforderungen und Qualifikation zu überbrücken. Wir stellen einen Ansatz zur intelligenten Assistenz vor, welcher auf einer kamerabasierten Erkennung von Arbeitsabläufen mit Hilfe von Methoden des maschinellen Lernens beruht. Das Assistenzsystem erzeugt automatisiert Hilfsmaterial zur Unterstützung der Werkenden. Zusätzlich zur Darstellung der technischen Aspekte, werden psychologische Aspekte, wie Akzeptanz und Motivation untersucht. KW - Assistenzsystem KW - Montage KW - Maschinelles Lernen Y1 - 2022 U6 - https://doi.org/10.33968/2022.20 PB - Hochschule für Technik, Wirtschaft und Kultur Leipzig ER -