OSRE: Object-to-Spot Rotation Estimation for Bike Parking Assessment

Current deep models excel in object detection for classification and localization. However, precise object rotation estimation within the visual context of an input image remains underexplored due to the lack of object datasets with rotation annotations. This paper addresses these challenges by tackling rotation estimation for parked bikes with respect to their parking area. Firstly, 3D graphics were leveraged to build a camera-agnostic well-annotated Synthetic Bike Rotation Dataset (SynthBRSet). Subsequently, an object-to-spot rotation estimator (OSRE) is introduced by extending object detection to regress bike rotations in two axes. As the proposed model trained purely on synthetic data, image smoothing techniques adopted during deployment on real-world images. The proposed OSRE has undergone evaluation on both synthetic and real-world data, showing promising results. Our data and code are available at
https://saghiralfasly.github.io/OSRE-Project/
.