TY - JOUR A1 - Agrawal, Shiva A1 - Bhanderi, Savankumar A1 - Elger, Gordon T1 - Semi-Automatic Annotation of 3D Radar and Camera for Smart Infrastructure-Based Perception JF - IEEE Access N2 - Environment perception using camera, radar, and/or lidar sensors has significantly improved in the last few years because of deep learning-based methods. However, a large group of these methods fall into the category of supervised learning, which requires a considerable amount of annotated data. Due to uncertainties in multi-sensor data, automating the data labeling process is extremely challenging; hence, it is performed manually to a large extent. Even though full automation of such a process is difficult, semiautomation can be a significant step to ease this process. However, the available work in this regard is still very limited; hence, in this paper, a novel semi-automatic annotation methodology is developed for labeling RGB camera images and 3D automotive radar point cloud data using a smart infrastructure-based sensor setup. This paper also describes a new method for 3D radar background subtraction to remove clutter and a new object category, GROUP, for radar-based object detection for closely located vulnerable road users. To validate the work, a dataset named INFRA-3DRC is created using this methodology, where 75% of the labels are automatically generated. In addition, a radar cluster classifier and an image classifier are developed, trained, and tested on this dataset, achieving accuracy of 98.26% and 94.86%, respectively. The dataset and Python scripts are available at https://fraunhoferivi.github.io/INFRA-3DRC-Dataset/. UR - https://doi.org/10.1109/ACCESS.2024.3373310 Y1 - 2024 UR - https://doi.org/10.1109/ACCESS.2024.3373310 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46180 SN - 2169-3536 VL - 12 SP - 34325 EP - 34341 PB - IEEE CY - New York 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 - 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 -