TY - GEN
T1 - A Sparse Gaussian Approach to Region-Based 6DoF Object Tracking
AU - Stoiber, Manuel
AU - Pfanne, Martin
AU - Strobl, Klaus H.
AU - Triebel, Rudolph
AU - Albu-Schäffer, Alin
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - We propose a novel, highly efficient sparse approach to region-based 6DoF object tracking that requires only a monocular RGB camera and the 3D object model. The key contribution of our work is a probabilistic model that considers image information sparsely along correspondence lines. For the implementation, we provide a highly efficient discrete scale-space formulation. In addition, we derive a novel mathematical proof that shows that our proposed likelihood function follows a Gaussian distribution. Based on this information, we develop robust approximations for the derivatives of the log-likelihood that are used in a regularized Newton optimization. In multiple experiments, we show that our approach outperforms state-of-the-art region-based methods in terms of tracking success while being about one order of magnitude faster. The source code of our tracker is publicly available (https://github.com/DLR-RM/RBGT ).
AB - We propose a novel, highly efficient sparse approach to region-based 6DoF object tracking that requires only a monocular RGB camera and the 3D object model. The key contribution of our work is a probabilistic model that considers image information sparsely along correspondence lines. For the implementation, we provide a highly efficient discrete scale-space formulation. In addition, we derive a novel mathematical proof that shows that our proposed likelihood function follows a Gaussian distribution. Based on this information, we develop robust approximations for the derivatives of the log-likelihood that are used in a regularized Newton optimization. In multiple experiments, we show that our approach outperforms state-of-the-art region-based methods in terms of tracking success while being about one order of magnitude faster. The source code of our tracker is publicly available (https://github.com/DLR-RM/RBGT ).
UR - http://www.scopus.com/inward/record.url?scp=85103255365&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-69532-3_40
DO - 10.1007/978-3-030-69532-3_40
M3 - Conference contribution
AN - SCOPUS:85103255365
SN - 9783030695316
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 666
EP - 682
BT - Computer Vision – ACCV 2020 - 15th Asian Conference on Computer Vision, 2020, Revised Selected Papers
A2 - Ishikawa, Hiroshi
A2 - Liu, Cheng-Lin
A2 - Pajdla, Tomas
A2 - Shi, Jianbo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th Asian Conference on Computer Vision, ACCV 2020
Y2 - 30 November 2020 through 4 December 2020
ER -