16.7.2.3 Vehicle Recogniton, Lidar, Laser Data, Depth Data

Chapter Contents (Back)
Vehicle Recognition. Vehicle Detection.

Cheng, H.H., Shaw, B.D., Palen, J., Lin, B., Chen, B., Wang, Z.,
Development and Field Test of a Laser-Based Nonintrusive Detection System for Identification of Vehicles on the Highway,
ITS(6), No. 2, June 2005, pp. 147-155.
IEEE Abstract. 0506
BibRef

Wang, T.[Tao], Zhu, Z.G.[Zhi-Gang], Taylor, C.N.[Clark N.],
A multimodal temporal panorama approach for moving vehicle detection, reconstruction and classification,
CVIU(117), No. 12, 2013, pp. 1724-1735.
Elsevier DOI 1310
BibRef
Earlier: A1, A2, Only:
Real time moving vehicle detection and reconstruction for improving classification,
WACV12(497-502).
IEEE DOI 1203
Laser-Doppler vibrometry
See also Intelligent multimodal and hyperspectral sensing for real-time moving target tracking. BibRef

Wender, S., Dietmayer, K.,
3D vehicle detection using a laser scanner and a video camera,
IET-ITS(2), No. 2, 2008, pp. 105-112.
DOI Link 1204
BibRef

Zhang, Z.X.[Zhao-Xiang], Tan, T.N.[Tie-Niu], Huang, K.Q.[Kai-Qi], Wang, Y.H.[Yun-Hong],
Three-Dimensional Deformable-Model-Based Localization and Recognition of Road Vehicles,
IP(21), No. 1, January 2012, pp. 1-13.
IEEE DOI 1112
BibRef
Earlier: A1, A3, A2, A4:
3D Model Based Vehicle Tracking Using Gradient Based Fitness Evaluation under Particle Filter Framework,
ICPR10(1771-1774).
IEEE DOI 1008

See also Model-Based Localization and Recognition of Road Vehicles. BibRef

Hu, Z.C.[Zhen-Cheng], Wang, C.H.[Chen-Hao], Uchimura, K.[Keiichi],
3D Vehicle Extraction and Tracking from Multiple Viewpoints for Traffic Monitoring by using Probability Fusion Map,
ELCVIA(7), No. 2, 2008, pp. xx-yy.
DOI Link 0903
BibRef

Misovic, D.S., Milic, S.D., Durovic, Z.M.,
Vessel Detection Algorithm Used in a Laser Monitoring System of the Lock Gate Zone,
ITS(17), No. 2, February 2016, pp. 430-440.
IEEE DOI 1602
Detection algorithms BibRef

Kim, S., Kim, H., Yoo, W., Huh, K.,
Sensor Fusion Algorithm Design in Detecting Vehicles Using Laser Scanner and Stereo Vision,
ITS(17), No. 4, April 2016, pp. 1072-1084.
IEEE DOI 1604
Algorithm design and analysis BibRef

Yu, Y.T.[Yong-Tao], Li, J.[Jonathan], Guan, H.Y.[Hai-Yan], Wang, C.[Cheng],
Automated Detection of Three-Dimensional Cars in Mobile Laser Scanning Point Clouds Using DBM-Hough-Forests,
GeoRS(54), No. 7, July 2016, pp. 4130-4142.
IEEE DOI 1606
Automobiles
See also Marked Point Process for Automated Tree Detection from Mobile Laser Scanning Point Cloud Data, A.
See also Semiautomated Extraction of Street Light Poles From Mobile LiDAR Point-Clouds. BibRef

Hata, A.Y., Wolf, D.F.,
Feature Detection for Vehicle Localization in Urban Environments Using a Multilayer LIDAR,
ITS(17), No. 2, February 2016, pp. 420-429.
IEEE DOI 1602
Asphalt BibRef

Asvadi, A.[Alireza], Garrote, L.[Luis], Premebida, C.[Cristiano], Peixoto, P.[Paulo], Nunes, U.J.[Urbano J.],
Multimodal vehicle detection: fusing 3D-LIDAR and color camera data,
PRL(115), 2018, pp. 20-29.
Elsevier DOI 1812
Multimodal data, Deep learning, Object detection, Fusion BibRef

Liu, K., Wang, W., Tharmarasa, R., Wang, J.,
Dynamic Vehicle Detection With Sparse Point Clouds Based on PE-CPD,
ITS(20), No. 5, May 2019, pp. 1964-1977.
IEEE DOI 1905
Vehicle dynamics, Pose estimation, Vehicle detection, Computational modeling, Dynamics, Laser radar, pose estimation BibRef

John, V., Liu, Z., Mita, S., Xu, Y.,
Stereo vision-based vehicle localization in point cloud maps using multiswarm particle swarm optimization,
SIViP(13), No. 4, June 2019, pp. 805-812.
WWW Link. 1906
BibRef

Nguyen, V.D., Tran, D.T., Byun, J.Y., Jeon, J.W.,
Real-Time Vehicle Detection Using an Effective Region Proposal-Based Depth and 3-Channel Pattern,
ITS(20), No. 10, October 2019, pp. 3634-3646.
IEEE DOI 1910
Proposals, Vehicle detection, Real-time systems, Object detection, Neural networks, Cameras, Deep learning, region proposal network, local pattern BibRef

An, J.[Jhonghyun], Kim, E.T.[Eun-Tai],
Novel Vehicle Bounding Box Tracking Using a Low-End 3D Laser Scanner,
ITS(22), No. 6, June 2021, pp. 3403-3419.
IEEE DOI 2106
Target tracking, Size measurement, Radar tracking, Position measurement, Lasers, Laser scanner, vehicle bounding box tracking BibRef

Coenen, M.[Max], Rottensteiner, F.[Franz],
Pose estimation and 3D reconstruction of vehicles from stereo-images using a subcategory-aware shape prior,
PandRS(181), 2021, pp. 27-47.
Elsevier DOI 2110
Vehicle detection, 3D vehicle reconstruction, Pose estimation, Multi-branch CNN, Active shape model BibRef

Wang, L.Y.[Lu-Yang], Lan, J.H.[Jin-Hui],
Adaptive Polar-Grid Gaussian-Mixture Model for Foreground Segmentation Using Roadside LiDAR,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
Roadside LiDAR for vehicles and pedestrians. BibRef

Zhao, K.[Kun], Ma, L.F.[Ling-Fei], Meng, Y.[Yu], Liu, L.[Li], Wang, J.[Junbo], Junior, J.M.[José Marcato], Gonçalves, W.N.[Wesley Nunes], Li, J.[Jonathan],
3D Vehicle Detection Using Multi-Level Fusion from Point Clouds and Images,
ITS(23), No. 9, September 2022, pp. 15146-15154.
IEEE DOI 2209
Point cloud compression, Feature extraction, Detectors, Proposals, Shape, 3D vehicle detection, deep learning, autonomous driving, data fusion BibRef

Zhou, S.L.[Shang-Lian], Xu, H.[Hao], Zhang, G.H.[Guo-Hui], Ma, T.W.[Tian-Wei], Yang, Y.[Yin],
Leveraging Deep Convolutional Neural Networks Pre-Trained on Autonomous Driving Data for Vehicle Detection from Roadside LiDAR Data,
ITS(23), No. 11, November 2022, pp. 22367-22377.
IEEE DOI 2212
Laser radar, Vehicle detection, Autonomous vehicles, Feature extraction, Point cloud compression, Training, roadside LiDAR data BibRef

Jin, X.J.[Xian-Jian], Yang, H.[Hang], He, X.K.[Xiong-Kui], Liu, G.H.[Guo-Hua], Yan, Z.[Zeyuan], Wang, Q.[Qikang],
Robust LiDAR-Based Vehicle Detection for On-Road Autonomous Driving,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
BibRef

Zhang, J.[Jian], Xie, H.[Hongtu], Zhang, L.[Lin], Lu, Z.[Zheng],
Information Extraction and Three-Dimensional Contour Reconstruction of Vehicle Target Based on Multiple Different Pitch-Angle Observation Circular Synthetic Aperture Radar Data,
RS(16), No. 2, 2024, pp. 401.
DOI Link 2402
BibRef


Duggal, S.[Shivam], Wang, Z.[Zihao], Ma, W.C.[Wei-Chiu], Manivasagam, S.[Sivabalan], Liang, J.[Justin], Wang, S.[Shenlong], Urtasun, R.[Raquel],
Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wild,
WACV22(277-286)
IEEE DOI 2202
Training, Solid modeling, Shape, Computational modeling, 3D Computer Vision BibRef

Kim, J.[Jinhyeong], Kim, Y.[Youngseok], Kum, D.[Dongsuk],
Low-level Sensor Fusion for 3d Vehicle Detection Using Radar Range-azimuth Heatmap and Monocular Image,
ACCV20(III:388-402).
Springer DOI 2103
BibRef

Saleh, K., Abobakr, A., Attia, M., Iskander, J., Nahavandi, D., Hossny, M., Nahvandi, S.,
Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Data,
TASKCV19(3235-3242)
IEEE DOI 2004
neural nets, object detection, optical radar, radar computing, radar imaging, vehicle detection, 3D LiDAR sensors, sim2real BibRef

Busch, S.,
Active Shape Model Precision Analysis of Vehicle Detection in 3d Lidar Point Clouds,
Semantics3D19(21-26).
DOI Link 1912
BibRef

Nagy, B.[Balázs], Benedek, C.[Csaba],
Real-Time Point Cloud Alignment for Vehicle Localization in a High Resolution 3D Map,
CVRoads18(I:226-239).
Springer DOI 1905
BibRef

Coenen, M., Rottensteiner, F.,
Probabilistic Vehicle Reconstruction Using a Multi-Task CNN,
CVRSUAD19(822-831)
IEEE DOI 2004
convolutional neural nets, edge detection, image reconstruction, image retrieval, learning (artificial intelligence), lighting, Pose Estimation BibRef

Coenen, M., Rottensteiner, F., Heipke, C.,
Detection And 3D Modelling of Vehicles From Terrestrial Stereo Image Pairs,
Hannover17(505-512).
DOI Link 1805
BibRef

Bulatov, D., Schilling, H.,
Segmentation methods for detection of stationary vehicles in combined elevation and optical data,
ICPR16(603-608)
IEEE DOI 1705
Adaptive optics, Automobiles, Image reconstruction, Image segmentation, Optical distortion, Optical imaging, Three-dimensional, displays BibRef

Chabot, F., Chaouch, M., Rabarisoa, J., Teuliere, C., Chateau, T.,
Accurate 3D car pose estimation,
ICIP16(3807-3811)
IEEE DOI 1610
Automobiles BibRef

Schlichting, A., Brenner, C.,
Vehicle Localization By Lidar Point Correlation Improved By Change Detection,
ISPRS16(B1: 703-710).
DOI Link 1610
BibRef

Farrugia, T.[Trevor], Barbarar, J.[Jonathan],
Pose Normalisation for 3D Vehicles,
CAIP15(I:235-245).
Springer DOI 1511
BibRef

Zia, M.Z.[Muhammad Zeeshan], Stark, M.[Michael], Schindler, K.[Konrad],
Are Cars Just 3D Boxes? Jointly Estimating the 3D Shape of Multiple Objects,
CVPR14(3678-3685)
IEEE DOI 1409
3D object recognition; Scene understanding BibRef

Lin, Y.L.[Yen-Liang], Morariu, V.I.[Vlad I.], Hsu, W.[Winston], Davis, L.S.[Larry S.],
Jointly Optimizing 3D Model Fitting and Fine-Grained Classification,
ECCV14(IV: 466-480).
Springer DOI 1408
Car dataset. BibRef

Hsiao, E.[Edward], Sinha, S.N.[Sudipta N.], Ramnath, K.[Krishnan], Baker, S.[Simon], Zitnick, L.[Larry], Szeliski, R.S.[Richard S.],
Car make and model recognition using 3D curve alignment,
WACV14(1-1)
IEEE DOI 1406
BibRef
And: A3, A2, A6, A1, Only: WACV14(285-292)
IEEE DOI 1406
Cameras BibRef

Zheng, T.L.[Tan Lun], Xia, L.M.[Li-Min], Liu, Y.F.[Yan-Fei],
Segmentation of urban traffic scene based on 3D structure,
IASP11(240-243).
IEEE DOI 1112
BibRef

Buch, N.[Norbert], Orwell, J.[James], Velastin, S.A.[Sergio A.],
3d Extended Histogram of Oriented Gradients (3DHoG) for Classification of Road Users in Urban Scenes,
BMVC09(xx-yy).
PDF File.
PDF File.
WWW Link.
WWW Link. 0909
3D descriptions to recognize pedestrians and cars. BibRef

Mohottala, S.[Shirmila], Ono, S.[Shintaro], Kagesawa, M.[Masataka], Ikeuchi, K.[Katsushi],
Fusion of a camera and a laser range sensor for vehicle recognition,
OTCBVS09(16-23).
IEEE DOI 0906
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Vehicle Counting .


Last update:Mar 16, 2024 at 20:36:19