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EnglishLeading causes of PTW (Powered Two-Wheeler) crashes and near misses in urban areas are on the part of a failure or delayed prediction of the changing trajectories of other vehicles. Regrettably, misperception from both car drivers and motorcycle riders results in fatal or serious consequences for riders. Intelligent vehicles could provide early warning about possible collisions, helping to avoid the crash. There is evidence that stereo cameras can be used for estimating the heading angle of other vehicles, which is key to anticipate their imminent location, but there is limited heading ground truth data available in the public domain. Consequently, we employed a marker-based technique for creating ground truth of car pose and create a dataset for computer vision benchmarking purposes. This dataset of a moving vehicle collected from a static mounted stereo camera is a simplification of a complex and dynamic reality, which serves as a test bed for car pose estimation algorithms. The dataset contains the accurate pose of the moving obstacle, and realistic imagery including texture-less and non-lambertian surfaces (e.g. reflectance and transparency).
| Titolo: | First stereo video dataset with ground truth for remote car pose estimation using satellite markers |
| Autori di Ateneo: | |
| Autori: | Gustavo Gil; Giovanni Savino; Marco Pierini |
| Data di pubblicazione: | 2017 |
| Titolo del libro: | ICMV Conference - International Conference on Machine Vision |
| Titolo del congresso: | The 10th International Conference on Machine Vision |
| Luogo del congresso: | Vienna, Austria |
| Data del congresso: | 13-15 November 2017 |
| Abstract: | Leading causes of PTW (Powered Two-Wheeler) crashes and near misses in urban areas are on the part of a failure or delayed prediction of the changing trajectories of other vehicles. Regrettably, misperception from both car drivers and motorcycle riders results in fatal or serious consequences for riders. Intelligent vehicles could provide early warning about possible collisions, helping to avoid the crash. There is evidence that stereo cameras can be used for estimating the heading angle of other vehicles, which is key to anticipate their imminent location, but there is limited heading ground truth data available in the public domain. Consequently, we employed a marker-based technique for creating ground truth of car pose and create a dataset for computer vision benchmarking purposes. This dataset of a moving vehicle collected from a static mounted stereo camera is a simplification of a complex and dynamic reality, which serves as a test bed for car pose estimation algorithms. The dataset contains the accurate pose of the moving obstacle, and realistic imagery including texture-less and non-lambertian surfaces (e.g. reflectance and transparency). |
| Handle: | http://hdl.handle.net/2158/1121067 |
| Appare nelle tipologie: | 4a - Articolo in atti di congresso |
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| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| MachineVisionConference_GG.pdf | PDF editoriale | DRM non definito | Accesso pubblicoVisualizza/Apri |
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