The semantic segmentation of a bird’s-eye view(BEV)is crucial for environment perception in autonomous driving,which includes the static elements of the scene,such as drivable areas,and dynamic elements such as cars....The semantic segmentation of a bird’s-eye view(BEV)is crucial for environment perception in autonomous driving,which includes the static elements of the scene,such as drivable areas,and dynamic elements such as cars.This paper proposes an end-to-end deep learning architecture based on 3D convolution to predict the semantic segmentation of a BEV,as well as voxel semantic segmentation,from monocular images.The voxelization of scenes and feature transformation from the perspective space to camera space are the key approaches of this model to boost the prediction accuracy.The effectiveness of the proposed method was demonstrated by training and evaluating the model on the NuScenes dataset.A comparison with other state-of-the-art methods showed that the proposed approach outperformed other approaches in the semantic segmentation of a BEV.It also implements voxel semantic segmentation,which cannot be achieved by the state-of-the-art methods.展开更多
This paper presents a travel-aid system for sight-impaired pedestrians in outdoor environment.Unlike many existing travel-aid systems that rely on stereo-vision,the proposed system aims to detect generic obstacles in ...This paper presents a travel-aid system for sight-impaired pedestrians in outdoor environment.Unlike many existing travel-aid systems that rely on stereo-vision,the proposed system aims to detect generic obstacles in a cluttered road environment by using just the single camera attached to user's body.To achieve this goal,the original image is re-sampled and mapped to a bird-eye view virtual plane,on which background edges are sub-sampled while obstacle edges are oversampled.Morphology filters with connected component analysis are used to enhance obstacle edges as edge-blobs with larger size,whereas sparse edges from background are filtered out.Based on the identified obstacles,safe walking areas are estimated by calculating a polar edge histogram.The algorithm is tested in different sidewalk scenes with complex pavements,and its efficiency has been confirmed.展开更多
基金the National Natural Science Founda-tion of China(No.52072243)the Sichuan Science and Technology Program(No.2020YFSY0058)。
文摘The semantic segmentation of a bird’s-eye view(BEV)is crucial for environment perception in autonomous driving,which includes the static elements of the scene,such as drivable areas,and dynamic elements such as cars.This paper proposes an end-to-end deep learning architecture based on 3D convolution to predict the semantic segmentation of a BEV,as well as voxel semantic segmentation,from monocular images.The voxelization of scenes and feature transformation from the perspective space to camera space are the key approaches of this model to boost the prediction accuracy.The effectiveness of the proposed method was demonstrated by training and evaluating the model on the NuScenes dataset.A comparison with other state-of-the-art methods showed that the proposed approach outperformed other approaches in the semantic segmentation of a BEV.It also implements voxel semantic segmentation,which cannot be achieved by the state-of-the-art methods.
基金The MKE(The Ministry of Knowledge Economy),Korea,under the ITRC(Infor mation Technology Research Center)support programsupervised by the NIPA(National IT Industry Promotion Agency)(NIPA-2012-(C1090-1021-0010))The Brain Korea 21 Project in 2012
文摘This paper presents a travel-aid system for sight-impaired pedestrians in outdoor environment.Unlike many existing travel-aid systems that rely on stereo-vision,the proposed system aims to detect generic obstacles in a cluttered road environment by using just the single camera attached to user's body.To achieve this goal,the original image is re-sampled and mapped to a bird-eye view virtual plane,on which background edges are sub-sampled while obstacle edges are oversampled.Morphology filters with connected component analysis are used to enhance obstacle edges as edge-blobs with larger size,whereas sparse edges from background are filtered out.Based on the identified obstacles,safe walking areas are estimated by calculating a polar edge histogram.The algorithm is tested in different sidewalk scenes with complex pavements,and its efficiency has been confirmed.
