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[IEEE 2018 IEEE International Conference on Intelligent Transportation Systems (ITSC) - Maui, HI, USA (2018.11.4-2018.11.7)] 2018 21st International Conference on Intelligent Transportation Systems (ITSC) - Vehicle Detection and Localization using 3D LIDAR Point Cloud and Image Semantic Segmentation
摘要： This paper presents a real-time approach to detect and localize surrounding vehicles in urban driving scenes. We propose a multimodal fusion framework that processes both 3D LIDAR point cloud and RGB image to obtain robust vehicle position and size in a Bird's Eye View (BEV). Semantic segmentation from RGB images is obtained using our efficient Convolutional Neural Network (CNN) architecture called ERFNet. Our proposal takes advantage of accurate depth information provided by LIDAR and detailed semantic information processed from a camera. The method has been tested using the KITTI object detection benchmark. Experiments show that our approach outperforms or is on par with other state-of-the-art proposals but our CNN was trained in another dataset, showing a good generalization capability to any domain, a key point for autonomous driving.
关键词： localization,ERFNet,image semantic segmentation,KITTI,autonomous driving,vehicle detection,CNN,point cloud,multimodal fusion,3D LIDAR
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) - Cluj-Napoca (2018.9.6-2018.9.8)] 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) - A Deep Learning Approach For Pedestrian Segmentation In Infrared Images
摘要： Semantic segmentation in the context of traffic scenes has been vastly explored using different architectures for deep convolutional networks and color images. In the case of infrared images there is place for improvement and scientific contributions mainly due to the lack of data sets that contain baseline segmentations in the infrared domain. This paper proposes a method for real time infrared pedestrian segmentation using ERFNet. Within the context of the proposed method we study the effect of different basic image enhancement techniques on the performance of the segmentation. We enhance an existing dataset of infrared images with ground truth segmentations for pedestrians. Our experiments show that the proposed method is accurate and appropriate for real time applications.
关键词： pedestrian segmentation,ERFNet,infrared images,deep learning,image enhancement
更新于2025-09-04 15:30:14