Long Short-Term Memory Kalman Filters: Recurrent Neural Estimators for Pose Regularization
Coskun H, Achilles F, Dipietro R, Navab N, Tombari F (2017)
Publication Type: Conference contribution
Publication year: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2017-October
Pages Range: 5525-5533
Conference Proceedings Title: Proceedings of the IEEE International Conference on Computer Vision
Event location: Venice, ITA
ISBN: 9781538610329
Abstract
One-shot pose estimation for tasks such as body joint localization, camera pose estimation, and object tracking are generally noisy, and temporal filters have been extensively used for regularization. One of the most widely-used methods is the Kalman filter, which is both extremely simple and general. However, Kalman filters require a motion model and measurement model to be specified a priori, which burdens the modeler and simultaneously demands that we use explicit models that are often only crude approximations of reality. For example, in the pose-estimation tasks mentioned above, it is common to use motion models that assume constant velocity or constant acceleration, and we believe that these simplified representations are severely inhibitive. In this work, we propose to instead learn rich, dynamic representations of the motion and noise models. In particular, we propose learning these models from data using long shortterm memory, which allows representations that depend on all previous observations and all previous states. We evaluate our method using three of the most popular pose estimation tasks in computer vision, and in all cases we obtain state-of-the-art performance.
Involved external institutions
Ludwig-Maximilians-Universität (LMU)
Germany (DE)
Johns Hopkins University (JHU)
United States (USA) (US)
Technische Universität München (TUM)
Germany (DE)
Germany (DE)
Johns Hopkins University (JHU)
United States (USA) (US)
Technische Universität München (TUM)
Germany (DE)
How to cite
APA:
Coskun, H., Achilles, F., Dipietro, R., Navab, N., & Tombari, F. (2017). Long Short-Term Memory Kalman Filters: Recurrent Neural Estimators for Pose Regularization. In Proceedings of the IEEE International Conference on Computer Vision (pp. 5525-5533). Venice, ITA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Coskun, Huseyin, et al. "Long Short-Term Memory Kalman Filters: Recurrent Neural Estimators for Pose Regularization." Proceedings of the 16th IEEE International Conference on Computer Vision, ICCV 2017, Venice, ITA Institute of Electrical and Electronics Engineers Inc., 2017. 5525-5533.
BibTeX: Download