TY - CHAP A1 - Kreß, Viktor A1 - Jung, Janis A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Human Pose Estimation in Real Traffic Scenes T2 - 2018 IEEE Symposium Series on Computational Intelligence (SSCI) KW - Autonomes Fahrzeug KW - Fahrerassistenzsystem KW - Fußgänger KW - Radfahrer Y1 - 2018 U6 - https://doi.org/10.1109/SSCI.2018.8628660 SP - 518 EP - 523 PB - IEEE CY - Bangalore, India ER - TY - CHAP A1 - Kreß, Viktor A1 - Jung, Janis A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard ED - Draude, Claude ED - Lange, Martin ED - Sick, Bernhard T1 - Start Intention Detection of Cyclists using an LSTM Network T2 - INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge) N2 - In this article, we present an approach for start intention detection of cyclists based on their head trajectories. Therefore, we are using a network architecture based on Long Short-Term Memory (LSTM) cells, which is able to handle input sequences of different lengths. This is important because, for example, due to occlusions, cyclists often only become visible to approaching vehicles shortly before dangerous situations occur. Hence, the dependency of the results on the input sequence length is investigated. We use a dataset with 206 situations where cyclists were transitioning from waiting to moving that was recorded from a moving vehicle in inner-city traffic.With an input sequence length of 1.0 s we achieve an F1-score of 96.2% on average 0.680 s after the first movement of the bicycle. We obtain similar results for sequence lengths down to 0.2 s. For shorter sequences, the results regarding the F1-score and the mean detection time deteriorate considerably. KW - Fahrerassistenzsystem KW - Fahrrad Y1 - 2019 SN - 978-3-88579-689-3 U6 - https://doi.org/10.18420/inf2019_ws25 SN - 1617-5468 SP - 219 EP - 228 PB - Gesellschaft für Informatik e.V. CY - Bonn ER - TY - CHAP A1 - Kreß, Viktor A1 - Jung, Janis A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Pose Based Start Intention Detection of Cyclists N2 - In this work, we present a new approach for start intention detection of cyclists based on 3D human pose estimation to increase their safety in road traffic. Start intention detection is realized using sequences of frame-wise estimated 3D poses. The poses were obtained by image sequences recorded by a stereo camera mounted behind the windshield of a moving vehicle. For training and evaluation, a dataset with 206 starting cyclists was created in real traffic. We demonstrate the advantages of this approach by comparing it to an existing, solely head trajectory based method. In particular, we investigate the performance for different observed time horizons ranging from 0.12 s up to 1.0 s as inputs for the two methods. This is of special importance for the protection of cyclists in road traffic, as they often only become visible to approaching vehicles shortly before dangerous situations occur. With an input length of 1.0 s the solely head trajectory based approach detects starting motions on average 0.834 s after the first motion of the bicycle with an F1-score of 97.5 %. The pose based approach outperforms these results by achieving the same F1-score 0.135 s earlier. The advantages of the pose based method become even more obvious with shorter input lengths. With an input length of 0.12 s, the head based approach achieves an F1-score of 93.5 % after 2.37 s, while the same score is reached after 0.668 s using poses. KW - Fahrerassistenzsystem KW - Fahrrad Y1 - 2019 U6 - https://doi.org/10.1109/ITSC.2019.8917215 SP - 2381 EP - 2386 PB - IEEE CY - Auckland ER -