OSRE: Object-to-Spot Rotation Estimation for Bike Parking Assessment
Current deep models provide remarkable object detection in terms of object classification and localization. However, estimating object rotation with respect to other visual objects in the visual context of an input image still lacks deep studies due to the unavailability of object datasets with rota...
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Zusammenfassung: | Current deep models provide remarkable object detection in terms of object
classification and localization. However, estimating object rotation with
respect to other visual objects in the visual context of an input image still
lacks deep studies due to the unavailability of object datasets with rotation
annotations.
This paper tackles these two challenges to solve the rotation estimation of a
parked bike with respect to its parking area. First, we leverage the power of
3D graphics to build a camera-agnostic well-annotated Synthetic Bike Rotation
Dataset (SynthBRSet). Then, we propose an object-to-spot rotation estimator
(OSRE) by extending the object detection task to further regress the bike
rotations in two axes. Since our model is purely trained on synthetic data, we
adopt image smoothing techniques when deploying it on real-world images. The
proposed OSRE is evaluated on synthetic and real-world data providing promising
results. Our data and code are available at
\href{https://github.com/saghiralfasly/OSRE-Project}{https://github.com/saghiralfasly/OSRE-Project}. |
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DOI: | 10.48550/arxiv.2303.00725 |