Publication
Vehicle pose estimation via regression of semantic points of interest
Conference Article
Conference
IEEE International Symposium on Image and Signal Processing and Analysis (ISPA)
Edition
11th
Pages
6
Doc link
https://doi.org/10.1109/ISPA.2019.8868508
File
Abstract
In this paper we address the problem of extracting vehicle 3D pose from 2D RGB images. An accurate methodology is presented that is capable of locating 3D coordinates of 20 pre-defined semantic vehicle points of interest or keypoints from 2D information. The presented two-step pipeline provides a straightforward way of extracting three-dimensional information from planar images and avoiding also the usage of other sensor that would lead to a more expensive and hard to manage system. The main contribution of this work is the presented dedicated network architectures that are able to locate simultaneously occluded and visible semantic points of interest to convert these 2D points into 3D space in a simple but efficient way. The presented method uses a robust network based on Stack-Hourglass architecture for precise prediction of semantic 2D keypoints from vehicles even if they are occluded. Furthermore, in the second step another dedicated network converts the 2D points into 3D world coordinates and therefore, the 3D pose of the vehicle can be automatically extracted, outperforming state-of-the-art techniques in terms of accuracy.
Categories
pattern recognition.
Author keywords
Image processing, deep learning, 3D pose estimation, vehicle detection
Scientific reference
J. Garcia, A. Agudo and F. Moreno-Noguer. Vehicle pose estimation via regression of semantic points of interest, 11th IEEE International Symposium on Image and Signal Processing and Analysis, 2019, Dubrovnik, Croatia, pp. 6.
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