ABSTRACT
While existing visual recognition approaches, which rely on 2D images to train their underlying models, work well for object classification, recognizing the changing state of a 3D object requires addressing several additional challenges. This paper proposes an active visual recognition approach to this problem, leveraging camera pose data available on mobile devices. With this approach, the state of a 3D object, which captures its appearance changes, can be recognized in real time. Our novel approach selects informative video frames filtered by 6-DOF camera poses to train a deep learning model to recognize object state. We validate our approach through a prototype for Augmented Reality-assisted hardware maintenance.
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778.Google ScholarCross Ref
- Steven J Henderson and Steven K Feiner. 2007. Augmented reality for maintenance and repair (armar). Technical Report. Columbia Univ New York Dept of Computer Science.Google Scholar
- Apple Inc. 2018. Apple Developer Documentation. https://developer.apple.com/documentation .Google Scholar
- Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller. 2015. Multi-view convolutional neural networks for 3d shape recognition. In Proceedings of the IEEE international conference on computer vision. 945--953. Google ScholarDigital Library
Index Terms
- Pose-assisted Active Visual Recognition in Mobile Augmented Reality
Recommendations
2D face fitting-assisted 3D face reconstruction for pose-robust face recognition
Special issue on Digital Information ForensicsRecent face recognition algorithm can achieve high accuracy when the tested face samples are frontal. However, when the face pose changes largely, the performance of existing methods drop drastically. Efforts on pose-robust face recognition are highly ...
Face recognition across pose using view based active appearance models (VBAAMs) on CMU multi-PIE dataset
ICVS'08: Proceedings of the 6th international conference on Computer vision systemsIn this paper we address the challenge of performing face recognition of a probe set of non-frontal images by performing automatic pose correction using Active Appearance Models (AAMs) and matching against a set of enrollment gallery of frontal images. ...
Improved 3D assisted pose-invariant face recognition
ICASSP '09: Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal ProcessingRecent face recognition algorithm can achieve high accuracy when testing face samples are frontal. However, when face pose changes largely, the performance of existing methods drop drastically. In this paper, we propose an improved algorithm aiming at ...
Comments