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| Content Provider | IET Digital Library |
|---|---|
| Author | Yao, Shouwen Zhang, Jiahao Wang, Yu |
| Abstract | At present, visual recognition systems have acquired wide employment in the autonomous-driving area. The lack of fully featured benchmarks that mimic scenarios faced by autonomous-driving system is the core factor limiting the visual understanding of complex urban traffic scenes. However, to establish a dataset adequately captures the complexity of real-world urban traffics consuming time and effort. In order to solve these difficulties, authors involve virtual reality to develop a large-scale dataset, which trains and tests approaches for autonomous-driving vehicles. Using the label of the object in virtual scenes, the coordinate transformation of a 3D object to a 2D plane is calculated, which makes the label of the pixel block corresponding to the object in the 2D plane accessible. Their recording platform is equipped with video camera models, LiDAR model and positioning system. By using the pilot-in-the-loop method with driving simulator hardware and VR devices, the authors acquire and establish a large, diverse dataset comprising stereo video sequences recorded in streets and mountain roads from several different environments. Their pioneering method of using VR technology significantly mitigates the costs of acquisition of training data. Crucially, their effort exceeds previous attempts in terms of dataset size, scene variability and complexity. |
| Starting Page | 411 |
| Ending Page | 415 |
| Page Count | 5 |
| Volume Number | 2020 |
| e-ISSN | 20513305 |
| Issue Number | Issue 13, Jul (2020) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2020/13 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1206 |
| Journal | The Journal of Engineering |
| Publisher | The Institution of Engineering and Technology |
| Publisher Date | 2020-01-13 |
| Access Restriction | Open |
| Rights License | Creative Commons Attribution-Non Commercial-No Derivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/) |
| Subject Keyword | 2D Plane 3D Object 3D Object Annotations Autonomous-driving System Autonomous-driving Vehicle Cluttered Scenarios Complex Urban Traffic Scenes Computer Vision And Image Processing Technique Coordinate Transformation Dataset Size Driver Information System Driving Simulator Hardware Feature Extraction Image Annotation Image Recognition Image Sequence Image Sonsor Instance-level Semantic Auto-labelling Knowledge Engineering Technique Large-scale Dataset Learning in AI LiDAR Model Pilot-in-the-loop Method Pixel Block Pixel-level Semantic Auto-labelling Positioning System Real-world Urban Traffic Scenes Recording Platform Road Traffic Scene Variability Stereo Image Processing Stereo Video Sequence Traffic Engineering Computing Training Data Acquisition Cost Video Camera Model Video Cameras Video Signal Processing Virtual Reality Virtual Scenes Visual Recognition System Visual Understanding VR Device VR Technology VR-based Dataset |
| Content Type | Text |
| Resource Type | Article |
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