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 - THES A1 - Bhanderi, Savankumar T1 - Real-time semantic and instance segmentation of 3D radar point cloud for smart infrastructure-based road user detection N2 - The next generation 3 + 1D mmWave automotive Radar (Radio Detection and Ranging) sensors provide additional elevation information along with 2D object location and their relative radial velocity. They are proven to be robust against adverse weather and poor lighting conditions, while also having a long range sensing ability. Due to these advantages, they are widely used in research focusing on autonomous driving functions where scene awareness is a critical component in the decision-making process. However, most of existing literature suffers from one of two limitations: 1) the use of clustering-based approaches, which provide suboptimal performance, or 2) high inference time. Furthermore, the research involving the applicability of Radar in the emerging field of smart roadside perception is still limited. To bridge this gap, a novel roadside perception pipeline is proposed in this thesis, which is capable of real-time inference using automotive Radar point clouds. While deep learning has revolutionized image processing, its application to automotive Radar point cloud data, especially from smart roadside infrastructure units, remains under-explored. In this thesis, deep learning techniques are used for semantic and instance segmentation on 3 + 1D Radar point cloud data, targeting key road users such as person, bicycle, motorcycle, car, and bus. Additionally, the background class is also considered to account for the Radar clutter. Due to the real-time inference requirements of roadside perception tasks, the proposed pipeline is constructed with as few parameters as possible while also maintaining the desired performance. The main components of the pipeline are MLP (Multi Layer Perceptron) and the self-attention mechanism, which are used for semantic and instance segmentation respectively. Additionally, exploiting the static field of view of the sensor, a 3D background subtraction method is applied to Radar point clouds to further reduce processing time. Experimental results demonstrate a 95.35% F 1 -macro averaged score for semantic segmentation, and 91.03% mAP (mean Average Precision) at an IoU (Intersection over Union) threshold of 0.5 for instance segmentation on a test set. Furthermore, a COCO (Common Objects in Context) mAP of 80.01% is achieved by the method, indicating high performance across a range of overlaps. The complete system, including the neural network, achieves an impressive inference time of just 22.93 milliseconds on an edge device (Nvidia Jetson AGX Orin), yielding a frame rate of 43.61, with a memory requirement of less than 0.7 MB. Thus, it is suitable for real-time roadside perception in ITS (Intelligent Transportation System). Y1 - 2024 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-51450 CY - Ingolstadt ER - TY - INPR A1 - Bhanderi, Savankumar A1 - Agrawal, Shiva A1 - Elger, Gordon T1 - Deep Segmentation of 3+1D Radar Point Cloud for Real-Time Roadside Traffic User Detection T2 - Research Square N2 - Smart cities rely on intelligent infrastructure to enhance road safety, optimize traffic flow, and enable vehicle-to-infrastructure (V2I) communication. A key component of such infrastructure is an efficient and real-time perception system that accurately detects diverse traffic participants. Among various sensing modalities, automotive radar is one of the best choices due to its robust performance in adverse weather and low-light conditions. However, due to low spatial resolution, traditional clustering-based approaches for radar object detection often struggle with vulnerable road user detection and nearby object separation. Hence, this paper proposes a deep learning-based 3+1D radar point cloud clustering methodology tailored for smart infrastructure-based perception applications. This approach first performs semantic segmentation of the radar point cloud, followed by instance segmentation to generate well-formed clusters with class labels using a deep neural network. It also detects single-point objects that conventional methods often miss. The described approach is developed and experimented using a smart infrastructure-based sensor setup and it performs segmentation of the point cloud in real-time. Experimental results demonstrate 95.35% F1-macro score for semantic segmentation and 91.03% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 for instance segmentation. Further, the complete pipeline operates at 43.61 frames per second with a memory requirement of less than 0.7 MB on the edge device (Nvidia Jetson AGX Orin). UR - https://doi.org/10.21203/rs.3.rs-7222130/v1 Y1 - 2025 UR - https://doi.org/10.21203/rs.3.rs-7222130/v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61996 SN - 2693-5015 PB - Research Square CY - Durham ER -