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.

Vehicle Trajectories from Unlabeled Data through Iterative Plane Registration / Federico Becattini, Lorenzo Seidenari, Lorenzo Berlincioni, Leonardo Galteri, Alberto Del Bimbo. - ELETTRONICO. - (2019), pp. 0-0. (Intervento presentato al convegno International Conference on Image Analysis and Processing) [10.1007/978-3-030-30642-7_6].

Vehicle Trajectories from Unlabeled Data through Iterative Plane Registration

Federico Becattini
;
Lorenzo Seidenari;BERLINCIONI, LORENZO;Leonardo Galteri;Alberto Del Bimbo
2019

Abstract

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.
2019
Image analysis and processing – ICIAP 2019
International Conference on Image Analysis and Processing
Federico Becattini, Lorenzo Seidenari, Lorenzo Berlincioni, Leonardo Galteri, Alberto Del Bimbo
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1174360
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