Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866908273852022784 |
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| author | Kunsági-Máté, Sándor Pető, Levente Seres, Lehel Matuszka, Tamás |
| author_facet | Kunsági-Máté, Sándor Pető, Levente Seres, Lehel Matuszka, Tamás |
| contents | 3D detection of traffic management objects, such as traffic lights and road signs, is vital for self-driving cars, particularly for address-to-address navigation where vehicles encounter numerous intersections with these static objects. This paper introduces a novel method for automatically generating accurate and temporally consistent 3D bounding box annotations for traffic lights and signs, effective up to a range of 200 meters. These annotations are suitable for training real-time models used in self-driving cars, which need a large amount of training data. The proposed method relies only on RGB images with 2D bounding boxes of traffic management objects, which can be automatically obtained using an off-the-shelf image-space detector neural network, along with GNSS/INS data, eliminating the need for LiDAR point cloud data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12620 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving Kunsági-Máté, Sándor Pető, Levente Seres, Lehel Matuszka, Tamás Computer Vision and Pattern Recognition 3D detection of traffic management objects, such as traffic lights and road signs, is vital for self-driving cars, particularly for address-to-address navigation where vehicles encounter numerous intersections with these static objects. This paper introduces a novel method for automatically generating accurate and temporally consistent 3D bounding box annotations for traffic lights and signs, effective up to a range of 200 meters. These annotations are suitable for training real-time models used in self-driving cars, which need a large amount of training data. The proposed method relies only on RGB images with 2D bounding boxes of traffic management objects, which can be automatically obtained using an off-the-shelf image-space detector neural network, along with GNSS/INS data, eliminating the need for LiDAR point cloud data. |
| title | Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.12620 |