A Voting Algorithm for Dynamic Object Identification and Pose Estimation

[1]  Roberto Cipolla,et al.  MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving , 2016, 2018 IEEE Intelligent Vehicles Symposium (IV).

[2]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[3]  Sultan Daud Khan.  Estimating Speeds and Directions of Pedestrians in Real-Time Videos: A solution to Road-Safety Problem , 2013, AgeingAI@AI*IA.

[4]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Jonathan Tompson,et al.  Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning , 2018, NeurIPS.

[6]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[7]  Ezeddin Al Hakim.  3D YOLO: End-to-End 3D Object Detection Using Point Clouds , 2018 .

[8]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Gregory D. Hager,et al.  Hierarchical semantic parsing for object pose estimation in densely cluttered scenes , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[10]  Ben Taskar,et al.  MODEC: Multimodal Decomposable Models for Human Pose Estimation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[12]  Bruce A. Draper,et al.  Introduction to the Bag of Features Paradigm for Image Classification and Retrieval , 2011, ArXiv.

[13]  Sascha Kolski.  Autonomous driving in dynamic environments , 2008 .

[14]  Andreas Geiger,et al.  Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art , 2017, Found. Trends Comput. Graph. Vis..

[15]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Peter V. Gehler,et al.  Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation , 2018, 2018 International Conference on 3D Vision (3DV).

[17]  Leonidas J. Guibas,et al.  ShapeNet: An Information-Rich 3D Model Repository , 2015, ArXiv.

[18]  T. Kanade,et al.  Reconstructing 3D Human Pose from 2D Image Landmarks , 2012, ECCV.

[19]  Kathleen M. Robinette,et al.  Civilian American and European Surface Anthropometry Resource (CAESAR), Final Report. Volume 1. Summary , 2002 .

[20]  Mustafa Mohamad,et al.  3D Object Recognition using Local Shape Descriptors , 2013 .

[21]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Michael J. Black,et al.  SMPL: A Skinned Multi-Person Linear Model , 2023 .

[23]  Bernt Schiele,et al.  DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model , 2016, ECCV.

[24]  Iasonas Kokkinos,et al.  DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Antonio M. López,et al.  The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Jianqiang Wang,et al.  Object Classification Using CNN-Based Fusion of Vision and LIDAR in Autonomous Vehicle Environment , 2018, IEEE Transactions on Industrial Informatics.

[27]  Yichen Wei,et al.  Towards 3D Human Pose Estimation in the Wild: A Weakly-Supervised Approach , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[28]  Shi-Wei Ma,et al.  Object recognition and pose estimation using appearance manifolds , 2013 .

[29]  Mark Everingham,et al.  Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation , 2010, BMVC.

[30]  Cristian Sminchisescu,et al.  Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[32]  Liang-Chia Chen,et al.  Novel 3-D Object Recognition Methodology Employing a Curvature-Based Histogram , 2013 .

[33]  Ying Wu,et al.  Deeply Learned Compositional Models for Human Pose Estimation , 2018, ECCV.

[34]  Yukinobu Taniguchi,et al.  The method based on view-directional consistency constraints for robust 3D object recognition , 2015, 2015 14th IAPR International Conference on Machine Vision Applications (MVA).

[35]  C. Urmson,et al.  Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[36]  Sebastian Thrun,et al.  SCAPE: shape completion and animation of people , 2005, SIGGRAPH 2005.

[37]  Markus Vincze,et al.  Ensemble of shape functions for 3D object classification , 2011, 2011 IEEE International Conference on Robotics and Biomimetics.

[38]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.

[39]  C. Urmson,et al.  Vehicle Detection and Tracking for the Urban Challenge , 2008 .

[40]  Marco Alexander Treiber.  An Introduction to Object Recognition: Selected Algorithms for a Wide Variety of Applications , 2010 .

[41]  Peter V. Gehler,et al.  Unite the People: Closing the Loop Between 3D and 2D Human Representations , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[42]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[43]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[44]  Stepán Obdrzálek,et al.  Object recognition methods based on transformation covariant features , 2004, 2004 12th European Signal Processing Conference.

[45]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[46]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[47]  Iasonas Kokkinos,et al.  DensePose: Dense Human Pose Estimation in the Wild , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[48]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[49]  Peter V. Gehler,et al.  Keep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image , 2016, ECCV.

[50]  Bernt Schiele,et al.  2D Human Pose Estimation: New Benchmark and State of the Art Analysis , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[51]  Long Chen,et al.  Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles , 2018 .

[52]  Kaiming He,et al.  Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[53]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[54]  Varun Ramakrishna,et al.  Convolutional Pose Machines , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[55]  Krystof Litomisky.  Consumer RGB-D Cameras and their Applications , 2012 .

[56]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.