Sappa, A.D.[Angel D.],
Dornaika, F.[Fadi],
Ponsa, D.,
Gerónimo, D.[David],
López, A.M.[Antonio M.],
An Efficient Approach to Onboard Stereo Vision System Pose Estimation,
ITS(9), No. 3, September 2008, pp. 476-490.
IEEE DOI
0809
BibRef
Ros, G.[German],
Sappa, A.D.[Angel D.],
Ponsa, D.[Daniel],
López, A.M.[Antonio M.],
Guerrero, J.[Julio],
Fast and Robust L_1-averaging-based Pose Estimation for Driving
Scenarios,
BMVC13(xx-yy).
DOI Link
1402
BibRef
Ross, R.[Robert],
Martchenko, A.[Andrew],
Devlin, J.[John],
A 3-degree of freedom binary search pose estimation technique,
MVA(24), No. 4, May 2013, pp. 769-776.
Springer DOI
1304
three fiduciary marker points. motor vehicle pose.
Compare to POSIT
See also Iterative Pose Estimation Using Coplanar Feature Points.
BibRef
Onkarappa, N.[Naveen],
Sappa, A.D.[Angel D.],
A Novel Space Variant Image Representation,
JMIV(47), No. 1-2, September 2013, pp. 48-59.
WWW Link.
1307
BibRef
Earlier:
Space Variant Representations for Mobile Platform Vision Applications,
CAIP11(II: 146-154).
Springer DOI
1109
BibRef
And:
On-Board Monocular Vision System Pose Estimation through a Dense
Optical Flow,
ICIAR10(I: 230-239).
Springer DOI
1006
BibRef
Sappa, A.D.[Angel D.],
Gerónimo, D.[David],
Dornaika, F.[Fadi],
López, A.M.[Antonio M.],
Real Time Vehicle Pose Using On-Board Stereo Vision System,
ICIAR06(II: 205-216).
Springer DOI
0610
BibRef
Dornaika, F.[Fadi],
Sappa, A.D.[Angel D.],
A featureless and stochastic approach to on-board stereo vision system
pose,
IVC(27), No. 9, 3 August 2009, pp. 1382-1393.
Elsevier DOI
0906
BibRef
Earlier: A2, A1:
Real-Time Vehicle Ego-Motion Using Stereo Pairs and Particle Filters,
ICIAR07(469-480).
Springer DOI
0708
On-board stereo vision system; Pose estimation; Featureless approach;
Particle filtering; Image warping
BibRef
Gu, H.Z.[Hui-Zhen],
Lee, S.Y.[Suh-Yin],
Car model recognition by utilizing symmetric property to overcome
severe pose variation,
MVA(24), No. 2, February 2013, pp. 255-274.
WWW Link.
1302
BibRef
Earlier:
Estimating initial pose by utilizing symmetric property for real-time
intelligent transportation system,
VCIP11(1-4).
IEEE DOI
1201
BibRef
Nilsson, J.,
Fredriksson, J.,
Odblom, A.C.E.,
Reliable Vehicle Pose Estimation Using Vision and a Single-Track
Model,
ITS(15), No. 6, December 2014, pp. 2630-2643.
IEEE DOI
1412
BibRef
Miao, Y.N.[Ya-Nan],
Tao, X.M.[Xiao-Ming],
Lu, J.H.[Jian-Hua],
Robust Monocular 3D Car Shape Estimation From 2D Landmarks,
CirSysVideo(28), No. 3, March 2018, pp. 652-663.
IEEE DOI
1804
BibRef
Earlier:
Robust 3D Car Shape Estimation from Landmarks in Monocular Image,
BMVC16(xx-yy).
HTML Version.
1805
cameras, convergence of numerical methods, inverse problems,
pose estimation, 3D shape, Stiefel manifold,
shape estimation
BibRef
Zhang, S.X.[Shan-Xin],
Wang, C.[Cheng],
He, Z.J.[Zi-Jian],
Li, Q.[Qing],
Lin, X.H.[Xiu-Hong],
Li, X.[Xin],
Zhang, J.Y.[Ju-Yong],
Yang, C.H.[Chen-Hui],
Li, J.[Jonathan],
Vehicle global 6-DoF pose estimation under traffic surveillance
camera,
PandRS(159), 2020, pp. 114-128.
Elsevier DOI
1912
Pose, 6-DoF, Surveillance camera, Dynamic 3D reconstruction,
Deep learning, Point clouds
BibRef
Bastian, B.T.[Blossom Treesa],
Victor, J.C.[Jiji Charangatt],
Detection and pose estimation of auto-rickshaws from traffic images,
MVA(31), No. 6, August 2020, pp. Article54.
WWW Link.
2008
BibRef
Wang, H.Q.[Han-Qi],
Wang, Z.L.[Zhi-Ling],
Lin, L.L.[Ling-Long],
Xu, F.Y.[Feng-Yu],
Yu, J.[Jie],
Liang, H.[Huawei],
Optimal Vehicle Pose Estimation Network Based on Time Series and
Spatial Tightness with 3D LiDARs,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link
2110
BibRef
Liu, R.J.[Rui-Jin],
Yuan, Z.[Zejian],
Liu, T.[Tie],
Learning TBox With a Cascaded Anchor-Free Network for Vehicle
Detection,
ITS(23), No. 1, January 2022, pp. 321-332.
IEEE DOI
2201
Vehicle detection, Task analysis, Feature extraction, Shape,
Robustness, Vehicle detection, anchor-free, pose estimation,
deep learning
BibRef
Yu, Y.[Ye],
Liu, H.T.[Hai-Tao],
Fu, Y.Z.[Yuan-Zi],
Jia, W.[Wei],
Yu, J.[Jun],
Yan, Z.S.[Zhi-Sheng],
Embedding Pose Information for Multiview Vehicle Model Recognition,
CirSysVideo(32), No. 8, August 2022, pp. 5467-5480.
IEEE DOI
2208
Feature extraction, Urban areas, Computational modeling,
Task analysis, Measurement, Integrated circuit modeling,
scale-aware features
BibRef
Kreiss, S.[Sven],
Bertoni, L.[Lorenzo],
Alahi, A.[Alexandre],
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and
Spatio-Temporal Association,
ITS(23), No. 8, August 2022, pp. 13498-13511.
IEEE DOI
2208
Pose estimation, Automobiles, Animals, Semantics,
Autonomous automobiles, Task analysis, Composite fields, pose tracking
BibRef
Wang, Q.[Qi],
Chen, J.[Jian],
Deng, J.Q.[Jian-Qiang],
Zhang, X.F.[Xin-Fang],
Zhang, K.X.[Kai-Xiang],
Simultaneous Pose Estimation and Velocity Estimation of an Ego
Vehicle and Moving Obstacles Using LiDAR Information Only,
ITS(23), No. 8, August 2022, pp. 12121-12132.
IEEE DOI
2208
Feature extraction, Pose estimation, Estimation, Laser radar,
Task analysis, Observers,
intelligent vehicle
BibRef
Wu, Z.W.[Zong-Wei],
Yuan, D.[Ding],
Zhang, F.G.[Feng-Gan],
Yao, M.[Minli],
Low-Cost Attitude Estimation Using GPS/IMU Fusion Aided by Land
Vehicle Model Constraints and Gravity-Based Angles,
ITS(23), No. 8, August 2022, pp. 13386-13402.
IEEE DOI
2208
Global Positioning System, Gyroscopes, Observability, Estimation,
Land vehicles, Sensors, Accelerometers, Attitude estimation,
attitude determination
BibRef
Magistri, S.[Simone],
Boschi, M.[Marco],
Sambo, F.[Francesco],
de Andrade, D.C.[Douglas Coimbra],
Simoncini, M.[Matteo],
Kubin, L.[Luca],
Taccari, L.[Leonardo],
de Luigi, L.[Luca],
Salti, S.[Samuele],
Lightweight and Effective Convolutional Neural Networks for Vehicle
Viewpoint Estimation From Monocular Images,
ITS(24), No. 1, January 2023, pp. 191-200.
IEEE DOI
2301
Estimation, Cameras, Predictive models, Azimuth, Adaptation models,
Roads, Detectors, Azimuth, convolutional neural networks, yaw
BibRef
Roch, P.[Peter],
Nejad, B.S.[Bijan Shahbaz],
Handte, M.[Marcus],
Marrón, P.J.[Pedro José],
Car Pose Estimation Through Wheel Detection,
ISVC21(I:265-277).
Springer DOI
2112
BibRef
Li, S.C.[Shi-Chao],
Yan, Z.Q.[Zeng-Qiang],
Li, H.Y.[Hong-Yang],
Cheng, K.T.[Kwang-Ting],
Exploring Intermediate Representation for Monocular Vehicle Pose
Estimation,
CVPR21(1873-1883)
IEEE DOI
2111
Training, Solid modeling, Laser radar,
Vehicle detection, Pose estimation, Transforms
BibRef
Koetsier, C.,
Peters, T.,
Sester, M.,
Learning the 3d Pose of Vehicles From 2d Vehicle Patches,
ISPRS20(B2:683-688).
DOI Link
2012
BibRef
Ke, L.[Lei],
Li, S.C.[Shi-Chao],
Sun, Y.[Yanan],
Tai, Y.W.[Yu-Wing],
Tang, C.K.[Chi-Keung],
Gsnet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and
Scene-aware Supervision,
ECCV20(XV:515-532).
Springer DOI
2011
BibRef
Khirodkar, R.[Rawal],
Yoo, D.H.[Dong-Hyun],
Kitani, K.M.[Kris M.],
Domain Randomization for Scene-Specific Car Detection and Pose
Estimation,
WACV19(1932-1940)
IEEE DOI
1904
object detection, pose estimation, real-world data distribution,
domain gap, appearance randomization, synthetic objects,
Task analysis
BibRef
Yan, K.,
Tian, Y.,
Wang, Y.,
Zeng, W.,
Huang, T.,
Exploiting Multi-grain Ranking Constraints for Precisely Searching
Visually-similar Vehicles,
ICCV17(562-570)
IEEE DOI
1802
image classification,
learning (artificial intelligence), pose estimation, probability,
Visualization
Dataset:
See also PKU-VD Dataset.
BibRef
Xue, Y.,
Qian, X.,
Vehicle detection and pose estimation by probabilistic representation,
ICIP17(3355-3359)
IEEE DOI
1803
Convolution, Mirrors, Pose estimation, Probabilistic logic, Training,
Visualization, Wheels, Fully convolutional network,
Vehicle pose estimation
BibRef
Chabot, F.,
Chaouch, M.,
Rabarisoa, J.,
Teuličre, C.,
Chateau, T.,
Deep MANTA: A Coarse-to-Fine Many-Task Network for Joint 2D and 3D
Vehicle Analysis from Monocular Image,
CVPR17(1827-1836)
IEEE DOI
1711
Object detection, Pose estimation, Proposals, Shape, Solid modeling
BibRef
Movshovitz-Attias, Y.[Yair],
Sheikh, Y.[Yaser],
Boddeti, V.N.[Vishnu Naresh],
Wei, Z.J.[Zi-Jun],
3D Pose-by-Detection of Vehicles via Discriminatively Reduced Ensembles
of Correlation Filters,
BMVC14(xx-yy).
HTML Version.
1410
BibRef
Hodlmoser, M.[Michael],
Micusik, B.[Branislav],
Pollefeys, M.[Marc],
Liu, M.Y.[Ming-Yu],
Kampel, M.[Martin],
Model-Based Vehicle Pose Estimation and Tracking in Videos Using
Random Forests,
3DV13(430-437)
IEEE DOI
1311
BibRef
Earlier: A1, A2, A4, A3, A5:
Classification and Pose Estimation of Vehicles in Videos by 3D Modeling
within Discrete-Continuous Optimization,
3DIMPVT12(198-205).
IEEE DOI
1212
Markov processes
BibRef
Rosebrock, D.[Dennis],
Rilk, M.[Markus],
Spehr, J.[Jens],
Wahl, F.M.[Friedrich M.],
Using the Shadow as a Single Feature for Real-Time Monocular Vehicle
Pose Determination,
ISVC11(I: 563-572).
Springer DOI
1109
BibRef
Hou, T.B.[Ting-Bo],
Wang, S.[Sen],
Qin, H.[Hong],
Vehicle matching and recognition under large variations of pose and
illumination,
OTCBVS09(24-29).
IEEE DOI
0906
See also Image Deconvolution With Multi-Stage Convex Relaxation and Its Perceptual Evaluation.
BibRef
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Vehicle Recogniton, Lidar, Laser Data, Depth Data .