Vehicle Trajectories from Unlabeled Data through Iterative Plane Registration
| Autor: | Leonardo Galteri, Alberto Del Bimbo, Lorenzo Berlincioni, Lorenzo Seidenari, Federico Becattini |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
trajectory prediction Computer science business.industry Plane (geometry) autonomous driving Context (language use) 02 engineering and technology Plan (drawing) autonomous driving trajectory prediction Set (abstract data type) Annotation 020901 industrial engineering & automation Trajectory Computer vision Artificial intelligence Single image business |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030306410 ICIAP (1) |
| Popis: | One of the most complex aspects of autonomous driving concerns understanding the surrounding environment. In particular, the interest falls on detecting which agents are populating it and how they are moving. The capacity to predict how these may act in the near future would allow an autonomous vehicle to safely plan its trajectory, minimizing the risks for itself and others. In this work we propose an automatic trajectory annotation method exploiting an Iterative Plane Registration algorithm based on homographies and semantic segmentations. The output of our technique is a set of holistic trajectories (past-present-future) paired with a single image context, useful to train a predictive model. |
| Databáze: | OpenAIRE |
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