Vehicle Pose Estimation Based on Convolutional Neural Network
Authors: 
Yifan Liu, Yao HeAuthors Info & Claims
Pages 81 - 87
Abstract
To address the problems of high computational cost for vehicle pose estimation methods based on binocular vision and lidar, a monocular vision pose estimation algorithm is proposed, and a multi-task convolutional neural network is designed as a network for vehicle position and pose estimation. Regard the target vehicle as a rigid body, model its size, attitude angle, and 3D center point separately, we train the model on the ApolloSpace dataset, and analyze the trained network to estimate the calculation speed and accuracy of the model. The results show that the pose estimation network presents superior accuracy and detection speed compared to two strong baseline models.
References
[1]
Claudine Badue, Rânik Guidolini, Raphael Vivacqua Carneiro, Pedro Azevedo, Vinicius Brito Cardoso, Avelino Forechi, Luan Jesus, Rodrigo Berriel, Thiago Meireles Paixao, Filipe Mutz, 2020. Self-driving cars: A survey. Expert Systems with Applications(2020), 113816.
[2]
Kunhai Cai, Yanling Tian, Fujun Wang, Dawei Zhang, Xianping Liu, and Bijan Shirinzadeh. 2017. Design and control of a 6-degree-of-freedom precision positioning system. Robotics and Computer-Integrated Manufacturing 44 (2017), 77–96.
[3]
Arindam Chaudhuri, Krupa Mandaviya, Pratixa Badelia, and Soumya K Ghosh. 2017. Optical character recognition systems. In Optical Character Recognition Systems for Different Languages with Soft Computing. Springer, 9–41.
[4]
Yu-Chun Chen, Te-Feng Su, and Shang-Hong Lai. 2014. Integrated vehicle and lane detection with distance estimation. In Asian Conference on Computer Vision. Springer, 473–485.
[5]
Dawei Du, Yuankai Qi, Hongyang Yu, Yifan Yang, Kaiwen Duan, Guorong Li, Weigang Zhang, Qingming Huang, and Qi Tian. 2018. The unmanned aerial vehicle benchmark: Object detection and tracking. In Proceedings of the European Conference on Computer Vision (ECCV). 370–386.
[6]
Kaiwen Duan, Song Bai, Lingxi Xie, Honggang Qi, Qingming Huang, and Qi Tian. 2019. Centernet: Keypoint triplets for object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 6569–6578.
[7]
Andreas Geiger, Philip Lenz, and Raquel Urtasun. 2012. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 3354–3361.
[8]
JW Hong. 2020. Why is artificial intelligence blamed more? Analysis of faulting artificial intelligence for self-driving car accidents in experimental settings. International Journal of Human–Computer Interaction 36, 18(2020), 1768–1774.
[9]
Xinyu Huang, Xinjing Cheng, Qichuan Geng, Binbin Cao, Dingfu Zhou, Peng Wang, Yuanqing Lin, and Ruigang Yang. 2018. The apolloscape dataset for autonomous driving. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 954–960.
[10]
Jason Ku, Melissa Mozifian, Jungwook Lee, Ali Harakeh, and Steven L Waslander. 2018. Joint 3d proposal generation and object detection from view aggregation. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 1–8.
[11]
Jason Ku, Alex D Pon, Sean Walsh, and Steven L Waslander. 2019. Improving 3d object detection for pedestrians with virtual multi-view synthesis orientation estimation. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 3459–3466.
[12]
Peiliang Li, Xiaozhi Chen, and Shaojie Shen. 2019. Stereo r-cnn based 3d object detection for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7644–7652.
[13]
Daniel Onoro-Rubio and Roberto J López-Sastre. 2016. Towards perspective-free object counting with deep learning. In European conference on computer vision. Springer, 615–629.
[14]
Ki-Yeong Park and Sun-Young Hwang. 2014. Robust range estimation with a monocular camera for vision-based forward collision warning system. The Scientific World Journal 2014 (2014).
[15]
Francisco Pérez-Hernández, Siham Tabik, Alberto Lamas, Roberto Olmos, Hamido Fujita, and Francisco Herrera. 2020. Object detection binary classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance. Knowledge-Based Systems 194 (2020), 105590.
[16]
Tong Qin, Peiliang Li, and Shaojie Shen. 2018. Vins-mono: A robust and versatile monocular visual-inertial state estimator. IEEE Transactions on Robotics 34, 4 (2018), 1004–1020.
[17]
Zengyi Qin, Jinglu Wang, and Yan Lu. 2019. Triangulation learning network: from monocular to stereo 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7615–7623.
[18]
Thomas Roddick, Alex Kendall, and Roberto Cipolla. 2018. Orthographic feature transform for monocular 3d object detection. arXiv preprint arXiv:1811.08188(2018).
[19]
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention. Springer, 234–241.
[20]
Xibin Song, Peng Wang, Dingfu Zhou, Rui Zhu, Chenye Guan, Yuchao Dai, Hao Su, Hongdong Li, and Ruigang Yang. 2019. Apollocar3d: A large 3d car instance understanding benchmark for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5452–5462.
[21]
Xudong Sun, Pengcheng Wu, and Steven CH Hoi. 2018. Face detection using deep learning: An improved faster RCNN approach. Neurocomputing 299(2018), 42–50.
[22]
Mingxing Tan and Quoc Le. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning. PMLR, 6105–6114.
[23]
Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon. 2018. Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV). 3–19.
[24]
Zhengyou Zhang. 2000. A flexible new technique for camera calibration. IEEE Transactions on pattern analysis and machine intelligence 22, 11(2000), 1330–1334.
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Published In
January 2022
239 pages
ISBN:9781450395571
DOI:10.1145/3512353
Copyright © 2022 ACM.
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Association for Computing Machinery
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Publication History
Published: 14 March 2022
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APIT 2022
APIT 2022: 2022 4th Asia Pacific Information Technology Conference
January 14 - 16, 2022
Virtual Event, Thailand
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