TY - CHAP A1 - Agrawal, Shiva A1 - Song, Rui A1 - Doycheva, Kristina A1 - Knoll, Alois A1 - Elger, Gordon ED - Klein, Cornel ED - Jarke, Matthias ED - Ploeg, Jeroen ED - Helfert, Markus ED - Berns, Karsten ED - Gusikhin, Oleg T1 - Intelligent Roadside Infrastructure for Connected Mobility T2 - Smart Cities, Green Technologies, and Intelligent Transport Systems: 11th International Conference, SMARTGREENS 2022 and 8th International Conference, VEHITS 2022: Revised Selected Papers UR - https://doi.org/10.1007/978-3-031-37470-8_6 Y1 - 2023 UR - https://doi.org/10.1007/978-3-031-37470-8_6 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-41761 SN - 978-3-031-37470-8 SN - 1865-0937 SP - 134 EP - 157 PB - Springer CY - Cham ER - TY - CHAP A1 - Agrawal, Shiva A1 - Bhanderi, Savankumar A1 - Amanagi, Sumit A1 - Doycheva, Kristina A1 - Elger, Gordon ED - Vinel, Alexey ED - Ploeg, Jeroen ED - Berns, Karsten ED - Gusikhin, Oleg T1 - Instance Segmentation and Detection of Children to Safeguard Vulnerable Traffic User by Infrastructure T2 - Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems N2 - Cameras mounted on intelligent roadside infrastructure units and vehicles can detect humans on the road using state-of-the-art perception algorithms, but these algorithms are presently not trained to distinguish between human and adult. However, this is a crucial requirement from a safety perspective because a child may not follow all the traffic rules, particularly while crossing the road. Moreover, a child may stop or may start playing on the road. In such situations, the separation of a child from an adult is necessary. The work in this paper targets to solve this problem by applying a transfer-learning-based neural network approach to classify child and adult separately in camera images. The described work is comprised of image data collection, data annotation, transfer learning-based model development, and evaluation. For the work, Mask-RCNN (region-based convolutional neural network) with different backbone architectures and two different baselines are investigated and the perception precision of the architectures after transfer-learning is compared. The results reveal that the best performing trained model is able to detect and classify children and adults separately in different road scenarios with segmentation mask AP (average precision) of 85% and bounding box AP of 92%. UR - https://doi.org/10.5220/0011825400003479 KW - Child and Adult Detection KW - Classification KW - Intelligent Roadside Infrastructure KW - Image Segmentation KW - Mask-RCNN KW - Traffic Flow Optimization KW - Transfer Learning Y1 - 2023 UR - https://doi.org/10.5220/0011825400003479 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-41785 SN - 978-989-758-652-1 SN - 2184-495X SP - 206 EP - 214 PB - SciTePress CY - Setúbal ER - TY - JOUR A1 - Senel, Numan A1 - Kefferpütz, Klaus A1 - Doycheva, Kristina A1 - Elger, Gordon T1 - Multi-Sensor Data Fusion for Real-Time Multi-Object Tracking JF - Processes N2 - Sensor data fusion is essential for environmental perception within smart traffic applications. By using multiple sensors cooperatively, the accuracy and probability of the perception are increased, which is crucial for critical traffic scenarios or under bad weather conditions. In this paper, a modular real-time capable multi-sensor fusion framework is presented and tested to fuse data on the object list level from distributed automotive sensors (cameras, radar, and LiDAR). The modular multi-sensor fusion architecture receives an object list (untracked objects) from each sensor. The fusion framework combines classical data fusion algorithms, as it contains a coordinate transformation module, an object association module (Hungarian algorithm), an object tracking module (unscented Kalman filter), and a movement compensation module. Due to the modular design, the fusion framework is adaptable and does not rely on the number of sensors or their types. Moreover, the method continues to operate because of this adaptable design in case of an individual sensor failure. This is an essential feature for safety-critical applications. The architecture targets environmental perception in challenging time-critical applications. The developed fusion framework is tested using simulation and public domain experimental data. Using the developed framework, sensor fusion is obtained well below 10 milliseconds of computing time using an AMD Ryzen 7 5800H mobile processor and the Python programming language. Furthermore, the object-level multi-sensor approach enables the detection of changes in the extrinsic calibration of the sensors and potential sensor failures. A concept was developed to use the multi-sensor framework to identify sensor malfunctions. This feature will become extremely important in ensuring the functional safety of the sensors for autonomous driving. UR - https://doi.org/10.3390/pr11020501 KW - environmental perception KW - sensor fusion KW - autonomous vehicle KW - unscented Kalman filter KW - object tracking KW - roadside units Y1 - 2023 UR - https://doi.org/10.3390/pr11020501 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-31989 SN - 2227-9717 VL - 11 IS - 2 PB - MDPI CY - Basel ER - TY - JOUR A1 - Agrawal, Shiva A1 - Bhanderi, Savankumar A1 - Doycheva, Kristina A1 - Elger, Gordon T1 - Static multi-target-based auto-calibration of RGB cameras, 3D Radar, and 3D Lidar sensors JF - IEEE Sensors Journal UR - https://doi.org/10.1109/JSEN.2023.3300957 KW - Autonomous vehicles KW - camera KW - feature extraction KW - intelligent roadside infrastructure KW - lidar KW - radar KW - sensor calibration Y1 - 2023 UR - https://doi.org/10.1109/JSEN.2023.3300957 SN - 1530-437X VL - 23 IS - 18 SP - 21493 EP - 21505 PB - IEEE CY - Piscataway ER -