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 - 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 - Reitberger, Günther A1 - Bieshaar, Maarten A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard A1 - Fuchs, Erich T1 - Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure T2 - 21st International Conference on Intelligent Transportation Systems (ITSC) 2018 N2 - 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. KW - Fahrerassistenzsystem KW - Fahrrad KW - Sensortechnik Y1 - 2018 U6 - https://doi.org/10.1109/ITSC.2018.8569267 PB - IEEE CY - Maui, HI, USA ER - TY - CHAP A1 - Zernetsch, Stefan A1 - Reichert, Hannes A1 - Kreß, Viktor A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks T2 - 2019 IEEE Intelligent Vehicles Symposium (IV) N2 - In this article, we present an approach to forecast trajectories of vulnerable road users (VRUs) including a numerical quantification of the uncertainty of the forecast. The uncertainty estimates are modeled as normal distributions by means of neural networks. Additionally, we present a method to evaluate the reliability of the forecasted uncertainty estimates, where we utilize quantile-quantile (Q-Q) plots, a graphical method to compare two distributions widely used in statistics. The positional accuracy is evaluated using Euclidean distances, in specific we use the average Euclidean error (AEE) and the average specific AEE (ASAEE). The model is trained and tested using a large dataset of 1311 cyclist trajectories, recorded at an urban intersection in real world traffic. Using this method, we achieve a similar positional accuracy compared to our previous work, where only positions are forecasted. The method is able to produce reliable uncertainty estimates for the motion types start, stop, turn left, and turn rightand produces underconfident uncertainty estimates for the motion types waitand move straight. Since uncertainties are not underestimated, the method can be used as a basis for trajectory planing in automated vehicles. KW - Fahrerassistenzsystem KW - Fahrrad KW - Autonomes Fahrzeug Y1 - 2019 U6 - https://doi.org/10.1109/IVS.2019.8814258 PB - IEEE CY - Paris, France 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 - Kress, Viktor A1 - Schreck, Steven A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks T2 - 2020 IEEE Symposium Series on Computational Intelligence (SSCI) N2 - This work investigates the use of knowledge about three dimensional (3D) poses and Recurrent Neural Networks (RNNs) for detection of basic movements, such as wait, start, move, stop, turn left, turn right, and no turn, of pedestrians and cyclists in road traffic. The 3D poses model the posture of individual body parts of these vulnerable road users (VRUs). Fields of application for this technology are, for example, driver assistance systems or autonomous driving functions of vehicles. In road traffic, VRUs are often occluded and only become visible in the immediate vicinity of the vehicle. Hence, our proposed approach is able to classify basic movements after different and especially short observation periods. The classification will then be successively improved in case of a longer observation. This allows countermeasures, such as emergency braking, to be initiated early if necessary. The benefits of using 3D poses are evaluated by a comparison with a method based solely on the head trajectory. We also investigate the effects of different observation periods. Overall, knowledge about 3D poses improves the basic movement detection, in particular for short observation periods. The greatest improvements are achieved for the basic movements start, stop, turn left, and turn right. KW - Fahrerassistenzsystem KW - Autonomes Fahrzeug KW - Fußgänger KW - Fahrrad Y1 - 2020 SN - 978-1-7281-2548-0 U6 - https://doi.org/10.1109/SSCI47803.2020.9308462 SP - 2723 EP - 2730 PB - IEEE CY - Canberra, Australia ER - TY - THES A1 - Zernetsch, Stefan T1 - Maschinelle Lernverfahren zur videobasierten Intentionserkennung von Radfahrern mit stationären Kameras KW - Maschinelles Lernen KW - Autonomes Fahrzeug KW - Fahrrad KW - Videoüberwachung Y1 - 2022 UR - https://kobra.uni-kassel.de/handle/123456789/13966 SN - 978-3-7376-1053-7 ER -