@inproceedings{KoehlerDollKebingeretal.2018, author = {K{\"o}hler, Sebastian and Doll, Konrad and Kebinger, Sophie and Schmitt, Daniel and Kr{\"o}hn, Michael and Fried, Maik and B{\"o}rsig, Rainer}, title = {Prozessinnovation: Hochautomatisiert lernendes Assistenzsystem f{\"u}r die manuelle Montage}, address = {K{\"o}ln}, isbn = {978-3-8007-4522-7}, pages = {153 -- 164}, year = {2018}, abstract = {Trotz hoher Automatisierungsgrade in der produzierenden Industrie, sind manuelle Montageprozesse durch den Menschen, sei es aufgrund der geforderten Flexibilit{\"a}t, insbesondere bei kleinen Losgr{\"o}ßen oder der erforderlichen Pr{\"a}zision, unverzichtbar. Um eine hohe Produktivit{\"a}t und niedrige Ausschussraten zu gew{\"a}hrleisten, ist es sinnvoll, den Menschen in seiner Montaget{\"a}tigkeit zu unterst{\"u}tzen oder zu entlasten. Intelligente Assistenzsysteme k{\"o}nnen den Menschen dahingehend unterst{\"u}tzen, dass sie bspw. schwere T{\"a}tigkeiten kollaborativ {\"u}bernehmen, Prozesse gezielt steuern oder Informationen bei Bedarf kontextsensitiv bereitstellen. In diesem Beitrag wird das Konzept eines intelligent hochautomatisiert lernenden Assistenzsystems vorgestellt, dessen Ziel es ist, anhand von bereits gelernten Abl{\"a}ufen an einem manuellen Montagearbeitsplatz fehlerhafte Abl{\"a}ufe zu erkennen oder neue valide Abl{\"a}ufe hochautomatisiert und un{\"u}berwacht zu lernen. Manuelle Montageprozesse bestehen aus Abfolgen von Handgriffen. Diese Handgriffe werden vom Assistenzsystem als Gesten erkannt und verarbeitet, sodass der Gesamtablauf in Teilschritte zerlegt werden kann. Weiterhin soll dieses Assistenzsystem Informationen zur Behebung des Fehlers direkt am Arbeitsplatz verf{\"u}gbar machen. Das Assistenzsystem, dessen Architektur in Abb. 1 dargestellt ist, besteht aus einem zustandsgesteuerten, lernenden Steuersystem mit einem Microsoft Kinect-v2-Sensor, der den Arbeitsplatz aus der {\"U}berkopfperspektive erfasst und die Montageschritte erkennt. Da das Grundprinzip des Assistenzsystems in der Langzeitbeobachtung und -unterst{\"u}tzung der Montaget{\"a}tigkeit liegt, werden die kontinuierlich aggregierten Daten zur Erweiterung und Verbesserung des Assistenzsystems genutzt. Es lernt somit hochautomatisiert neue oder abgewandelte Montageabl{\"a}ufe. Hieraus ergibt sich eine Herausforderung an die Bewertung der G{\"u}te eines solchen Systems. W{\"a}hrend eine hohe Erkennungsrate der einzelnen Aktivit{\"a}ten innerhalb eines Montageablaufs unabdingbar ist, wird f{\"u}r das vorgeschlagene System dar{\"u}ber hinaus gefordert, dass komplette Abl{\"a}ufe korrekt, d.h. in der richtigen Reihenfolge ohne einzelne Falschklassifikationen, erkannt werden. Nur hierdurch lassen sich real ge{\"a}nderte Abl{\"a}ufe hochautomatisiert und un{\"u}berwacht im Zustandsautomat des Steuersystems online einlernen.}, subject = {Automatisierungstechnik}, language = {de} } @inproceedings{KoehlerDollKebingeretal.2018, author = {K{\"o}hler, Sebastian and Doll, Konrad and Kebinger, Sophie and Schmitt, Daniel and Kr{\"o}hn, Michael and Fried, Maik and B{\"o}rsig, Rainer}, title = {Gestenerkennung in einem hochautomatisiert lernenden Assistenzsystem f{\"u}r manuelle Montageprozesse}, series = {AUTOMATION 2018, VDI-Berichte 2330}, booktitle = {AUTOMATION 2018, VDI-Berichte 2330}, address = {Baden-Baden}, isbn = {978-3-18-092330-7}, pages = {145 -- 156}, year = {2018}, subject = {Assistenzsystem}, language = {de} } @inproceedings{ReitbergerBieshaarZernetschetal.2018, author = {Reitberger, G{\"u}nther and Bieshaar, Maarten and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard and Fuchs, Erich}, title = {Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure}, series = {21st International Conference on Intelligent Transportation Systems (ITSC) 2018}, booktitle = {21st International Conference on Intelligent Transportation Systems (ITSC) 2018}, publisher = {IEEE}, address = {Maui, HI, USA}, doi = {10.1109/ITSC.2018.8569267}, year = {2018}, abstract = {In future traffic scenarios, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation based on data or information exchange. This article presents an approach to cooperative tracking of cyclists using smart devices and infrastructure-based sensors. A smart device is carried by the cyclists and an intersection is equipped with a wide angle stereo camera system. Two tracking models are presented and compared. The first model is based on the stereo camera system detections only, whereas the second model cooperatively combines the camera based detections with velocity and yaw rate data provided by the smart device. Our aim is to overcome limitations of tracking approaches based on single data sources. We show in numerical evaluations on scenes where cyclists are starting or turning right that the cooperation leads to an improvement in both the ability to keep track of a cyclist and the accuracy of the track particularly when it comes to occlusions in the visual system. We, therefore, contribute to the safety of vulnerable road users in future traffic.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{KressJungZernetschetal.2018, author = {Kreß, Viktor and Jung, Janis and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Human Pose Estimation in Real Traffic Scenes}, series = {2018 IEEE Symposium Series on Computational Intelligence (SSCI)}, booktitle = {2018 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher = {IEEE}, address = {Bangalore, India}, doi = {10.1109/SSCI.2018.8628660}, pages = {518 -- 523}, year = {2018}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{ZernetschKressSicketal.2018, author = {Zernetsch, Stefan and Kreß, Viktor and Sick, Bernhard and Doll, Konrad}, title = {Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network}, series = {2018 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2018 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Changshu, China}, doi = {10.1109/IVS.2018.8500428}, year = {2018}, abstract = {In this article, we present a novel approach to detect starting motions of cyclists in real world traffic scenarios based on Motion History Images (MHIs). The method uses a deep Convolutional Neural Network (CNN) with a residual network architecture (ResNet), which is commonly used in image classification and detection tasks. By combining MHIs with a ResNet classifier and performing a frame by frame classification of the MHIs, we are able to detect starting motions in image sequences. The detection is performed using a wide angle stereo camera system at an urban intersection. We compare our algorithm to an existing method to detect movement transitions of pedestrians that uses MHIs in combination with a Histograms of Oriented Gradients (HOG) like descriptor and a Support Vector Machine (SVM), which we adapted to cyclists. To train and evaluate the methods a dataset containing MHIs of 394 cyclist starting motions was created. The results show that both methods can be used to detect starting motions of cyclists. Using the SVM approach, we were able to safely detect starting motions 0.506 s on average after the bicycle starts moving with an F 1 -score of 97.7\%. The ResNet approach achieved an F 1- score of 100\% at an average detection time of 0.144 s. The ResNet approach outperformed the SVM approach in both robustness against false positive detections and detection time.}, subject = {Fahrerassistenzsystem}, 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} }