16.7.2.2 Vehicle Pose

Chapter Contents (Back)
Vehicle Pose. Vehicle Recognition. Vehicle Detection.

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

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.[Hanqi], 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


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 .


Last update:Jan 13, 2022 at 22:02:22