Factors Influencing Accuracy of Estimating Position of Objects in a Multi-camera System
DOI:
https://doi.org/10.26636/jtit.2024.2.1457Keywords:
3D imaging, measurement accuracy, optical imaging, stereovisionAbstract
Nowadays, research focusing on robotics, autonomous vehicles, and scene analysis shows a clear need for the ability to accurately reconstruct three-dimensional environments. One of the methods allowing to conduct such a reconstruction is to use a set of cameras and image processing techniques. This is a passive method. Despite being, in general, less accurate than its active counterparts, it offers significant advantages in numerous applications in which active systems cannot be deployed due to limited performance. This paper provides a theoretical analysis of the accuracy of estimating 3D positions of objects present at a given scene, based on images from a set of cameras. The analysis assumes a known geometrical configuration of the camera system. The important limiting factor in the considered scenario is the physical resolution of sensors - especially in the case of systems that are supposed to work in real time, with a high FPS rate, as the use of high-resolution cameras is difficult in such circumstances. In the paper, the influence of the geometric arrangement of the cameras is studied and important conclusions about the potential of three-camera configurations are drawn. The analysis performed and the formulas derived help predict the boundary accuracy values of any system using a digital camera. The results of an experiment that confirm the theoretical conclusions are presented as well.
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T. Grajek, R. Ratajczak, M. Domański, and K. Wegner, "Limitations of Vehicle Length Estimation Using Stereoscopic Video Analysis", 20th International Conference on Systems, Signals and Image Processing IWSSIP, Bucharest, Romania, pp. 27-30, 2013 (https:/doi.org/10.1109/IWSSIP.2013.6623441).
View in Google Scholar
R. Ratajczak, T. Grajek, K. Wegner, K. Klimaszewski, M. Kurc, and M. Domański, "Vehicle Dimensions Estimation Scheme Using AAM on Stereoscopic Video", 10th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS, Krakow, Poland, pp. 478-482, 2013 (https:/doi.org/10.1109/AVSS.2013.6636686).
View in Google Scholar
R.I. Hartley, "Theory and Practice of Projective Rectification", International Journal of Computer Vision, vol. 35, pp. 115-127, 1999 (https:/doi.org/10.1023/A:1008115206617).
View in Google Scholar
L. Traxler, L. Ginner, S. Breuss, and B. Blaschitz, "Experimental Comparison of Optical Inline 3D Measurement and Inspection Systems", IEEE Access, vol. 9, pp. 53952-53963, 2021 (https:/doi.org/10.1109/ACCESS.2021.3070381).
View in Google Scholar
L. Wang et al., "Parallax Attention for Unsupervised Stereo Correspondence Learning", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 4, pp. 2108-2125, 2022 (https:/doi.org/10.1109/TPAMI.2020.3026899).
View in Google Scholar
S. Wang et al., "Horizontal Attention Based Generation Module for Unsupervised Domain Adaptive Stereo Matching", IEEE Robotics and Automation Letters, vol. 8, no. 10, pp. 6779-6786, 2023 (https:/doi.org/10.1109/LRA.2023.3313009).
View in Google Scholar
N. Snavely, S.M. Seitz, and R. Szeliski, "Photo Tourism: Exploring Photo Collections in 3D", ACM Transactions on Graphics, vol. 25, no. 3, pp. 835-846, 2006 (https:/doi.org/10.1145/1141911.1141964).
View in Google Scholar
M. Yao and B. Xu, "A Dense Stereovision System for 3D Body Imaging", IEEE Access, vol. 7, pp. 170907-170918, 2019 (https:/doi.org/10.1109/ACCESS.2019.2955915).
View in Google Scholar
J. Odmins et al., "Comparison of Passive and Active Fiducials for Optical Tracking", Latvian Journal of Physics and Technical Sciences, vol. 59, no. 5, pp. 46-57, 2022 (https:/doi.org/10.2478/lpts-2022-0040).
View in Google Scholar
C. Ye et al., "Multi-viewpoint Optical Positioning Algorithm Based on Minimizing Reconstruction Error", Measurement, vol. 218, art. no. 113206, 2023 (https:/doi.org/10.1016/j.measurement.2023.113206).
View in Google Scholar
T. Zhang, J. Wang, S. Song, and M.Q.-H. Meng, "Wearable Surgical Optical Tracking System Based on Multi-Modular Sensor Fusion", IEEE Transactions on Instrumentation and Measurement, vol. 71, art no. 5006211, 2022 (https:/doi.org/10.1109/TIM.2022.3150828).
View in Google Scholar
V. Peretroukhin, J. Kelly, and T.D. Barfoot, "Optimizing Camera Perspective for Stereo Visual Odometry", 2014 Canadian Conference on Computer and Robot Vision, Montreal, Canada, 2014 (https:/doi.org/10.1109/CRV.2014.9).
View in Google Scholar
H. M. Becerra and C. Sagues, Visual Control of Wheeled Mobile Robots, Springer, 118 p., 2014 (https:/doi.org/10.1007/978-3-319-05783-5).
View in Google Scholar
D. Murray and J.J. Little, "Using Real-Time Stereo Vision for Mobile Robot Navigation", Autonomous Robots, vol. 8, no. 2, pp. 161-171, 2000 (https:/doi.org/10.1023/A:1008987612352).
View in Google Scholar
V. Tadić et al., "Application of the ZED Depth Sensor for Painting Robot Vision System Development", IEEE Access, vol. 9, pp. 117845-117859, 2021 (https:/doi.org/10.1109/ACCESS.2021.3105720).
View in Google Scholar
A. Geiger, P. Lenz, and R. Urtasun, "Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite", 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012 (https:/doi.org/10.1109/CVPR.2012.6248074).
View in Google Scholar
L. Yang et al., "Vehicle Speed Measurement Based on Binocular Stereovision System", IEEE Access, vol. 7, pp. 106628-106641, 2019 (https:/doi.org/10.1109/ACCESS.2019.2932120).
View in Google Scholar
L. Matthies and S. Shafer, "Error Modeling in Stereo Navigation", IEEE Journal on Robotics and Automation, vol. 3, no. 3, pp. 239-248, 1987 (https:/doi.org/10.1109/JRA.1987.1087097).
View in Google Scholar
C. Chang and S. Chatterjee, "Quantization Error Analysis in Stereo Vision", Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems & Computers, vol. 2, pp. 1037-1041, 1992 (https:/doi.org/10.1109/ACSSC.1992.269140).
View in Google Scholar
F. Fooladgar, S. Samavi, and S.M.R. Soroushmehr, "Geometrical Analysis of Altitude Estimation Error Caused by Pixel Quantization in Stereo Vision", 20th Iranian Conference on Electrical Engineering (ICEE2012), Tehran, Iran, 2012 (https:/doi.org/10.1109/IranianCEE.2012.6292444).
View in Google Scholar
F. Fooladgar, S. Samavi, S.M.R. Soroushmehr, and S. Shirani, "Geometrical Analysis of Localization Error in Stereo Vision Systems", IEEE Sensors Journal, vol. 13, no. 11, pp. 4236-4246, 2013 (https:/doi.org/10.1109/JSEN.2013.2264480).
View in Google Scholar
C. Freundlich, M. Zavlanos, and P. Mordohai, "Exact Bias Correction and Covariance Estimation for Stereo Vision", 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA, pp. 3296-3304, 2015 (https:/doi.org/10.1109/CVPR.2015.7298950).
View in Google Scholar
L. Yang, B. Wang, R. Zhang, H. Zhou, and R. Wang, "Analysis on Location Accuracy for the Binocular Stereo Vision System", IEEE Photonics Journal, vol. 10, no. 1, 2017 (https:/doi.org/10.1109/JPHOT.2017.2784958).
View in Google Scholar
W. Sankowski, M. Włodarczyk, D. Kacperski, and K. Grabowski, "Estimation of Measurement Uncertainty in Stereo Vision System", Image and Vision Computing, vol. 61, pp. 70-81, 2017 (https:/doi.org/10.1016/j.imavis.2017.02.005).
View in Google Scholar
D. Jin, Y. Yang, "Sensitivity Analysis of the Error Factors in the Binocular Vision Measurement System", Optical Engineering, vol. 57, no. 10, pp. 104-109, 2018 (https:/doi.org/10.1117/1.OE.57.10.104109).
View in Google Scholar
X. Liu, W. Chen, H. Madhusudanan, L. Du, and Y. Sun, "Camera Orientation Optimization in Stereo Vision Systems for Low Measurement Error", IEEE/ASME Transactions on Mechatronics, vol. 26, no. 2, pp. 1178-1182, 2021 (https:/doi.org/10.1109/TMECH.2020.3019305).
View in Google Scholar
S. Gao, X. Chen, X. Wu, T. Zeng, and X. Xie, "Analysis of Ranging Error of Parallel Binocular Vision System", 2020 IEEE International Conference on Mechatronics and Automation (ICMA), Beijing, China, pp. 621-625, 2020 (https:/doi.org/10.1109/ICMA49215.2020.9233770).
View in Google Scholar
D. Liaw, S. Zhao, and Y. Hu, "A Study of Image Processing Based Object Depth Estimation", 2019 19th International Conference on Control, Automation and Systems (ICCAS), Jeju, South Korea, pp. 949-954, 2019 (https:/doi.org/10.23919/ICCAS47443.2019.8971553).
View in Google Scholar
X. Guan, X. Li, J. Su, H. Pan, and L. Geng, "Integrative Evaluation of the Optimal Configuration for the Measurement of the Line Segments Using Stereo Vision", Optik, vol. 124, no. 11, pp. 1015-1018, 2013 (https:/doi.org/10.1016/J.IJLEO.2013.01.018).
View in Google Scholar
P. Pinggera, D. Pfeiffer, U. Franke, and R. Mester, "Know Your Limits: Accuracy of Long Range Stereoscopic Object Measurements in Practice", Proceedings of 13th European Conference on Computer Vision, Zurich, Switzerland, 2014 (https:/doi.org/0.1007/978-3-319-10605-2_7).
View in Google Scholar
L. Yu and G. Lubineau, "Modeling of Systematic Errors in Stereo-digital Image Correlation Due to Camera Self-heating", Scientific Reports, vol. 9, art. no. 6567, 2019 (https:/doi.org/10.1038/s41598-019-43019-7).
View in Google Scholar
U.R. Dhond and J.K. Aggarwal, "Binocular Versus Trinocular Stereo", Proceedings of IEEE International Conference on Robotics and Automation, vol. 3, pp. 2045-2050, 1990 (https:/doi.org/10.1109/ROBOT.1990.126306).
View in Google Scholar
J. Wang et al., "Stereo Matching Optimization with Multi-baseline Trinocular Camera Model", 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), London, Canada, 2020 (https:/doi.org/10.1109/CCECE47787.2020.9255786).
View in Google Scholar
S. Bi et al., "High Precision Optical Tracking System Based on Near Infrared Trinocular Stereo Vision", Sensors, vol. 21, no. 7, 2021 (https:/doi.org/10.3390/s21072528).
View in Google Scholar
Y. Gao, H. Cui, X. Wang, and Z. Huang, "Novel Precision Vision Measurement Method Between Area-Array Imaging and Linear-Array Imaging Especially for Dynamic Objects", IEEE Transactions on Instrumentation and Measurement, vol. 71, art. no. 5022609, 2022 (https:/doi.org/10.1109/TIM.2022.3207826).
View in Google Scholar
W. Förstner and B.P. Wrobel, Photogrammetric Computer Vision, Springer Cham, 816 p., 2016 (https:/doi.org/10.1007/978-3-319-11550-4).
View in Google Scholar
B. Cyganek and J.P. Siebert, An Introduction to 3D Computer Vision Techniques and Algorithms, Hoboken: Wiley, 512 p., 2009 (https:/doi.org/10.1002/9780470699720).
View in Google Scholar
D. Mieloch and A. Grzelka, "Segmentation-based Method of Increasing the Depth Maps Temporal Consistency", International Journal of Electronics and Telecommunications, vol. 64, no. 3, pp. 293-298, 2018 (https:/doi.org/10.24425/123521).
View in Google Scholar
H. Xu and J. Zhang, "AANet: Adaptive Aggregation Network for Efficient Stereo Matching", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, pp. 1956-1965, 2020 (https:/doi.org/10.1109/CVPR42600.2020.00203).
View in Google Scholar
O. Stankiewicz and M. Domański, "Depth Map Estimation based on Maximum a Posteriori Probability", IEIE Transactions on Smart Processing and Computing, vol. 7, no. 1, pp. 49-61, 2018 (https:/doi.org/10.5573/IEIESPC.2018.7.1.049).
View in Google Scholar
D. Mieloch, A. Dziembowski, A. Grzelka, O. Stankiewicz, and M. Domański, "Graph-based Multiview Depth Estimation Using Segmentation", IEEE International Conference on Multimedia and Expo ICME, Hong Kong, China, pp. 217-222, 2017 (https:/doi.org/10.1109/ICME.2017.8019532).
View in Google Scholar
R. Brandt, N. Strisciuglio, N. Petkov, and M. Wilkinson, "Efficient Binocular Stereo Correspondence Matching with 1-D Max-trees", Pattern Recognition Letters, vol. 135, pp. 402-408, 2020 (https:/doi.org/10.1016/j.patrec.2020.02.019).
View in Google Scholar
B. Busam, M. Hog, S. McDonagh, and G. Slabaugh, "SteReFo: Efficient Image Refocusing with Stereo Vision", 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, South Korea, pp. 3295-3304, 2019 (https:/doi.org/10.1109/ICCVW.2019.00411).
View in Google Scholar
B. Wilburn et al., "High Performance Imaging Using Large Camera Arrays", ACM Transactions on Graphics, vol. 24, no. 3, pp. 765-776, 2005 (https:/doi.org/10.1145/1073204.1073259).
View in Google Scholar
C. Heinze, S. Spyropoulos, S. Hussmann, and C. Perwass, "Automated Robust Metric Calibration Algorithm for Multifocus Plenoptic Cameras", IEEE Transactions on Instrumentation and Measurement, vol. 65, no. 5, pp. 1197-1205, 2016 (https:/doi.org/10.1109/TIM.2015.2507412).
View in Google Scholar
O. Stankiewicz et al., "A Free-viewpoint Television System for Horizontal Virtual Navigation", IEEE Transactions on Multimedia, vol. 20, no. 8, pp. 2182-2195, 2018 (https:/doi.org/10.1109/TMM.2018.2790162).
View in Google Scholar
N.S. Nair and M.S. Nair, "Scalable Multi-view Stereo Using CMA-ES and Distance Transform-based Depth Map Refinement", 13th International Conference on Machine Vision, Rome, Italy, 2021 (https:/doi.org/10.1117/12.2587241).
View in Google Scholar
Z. Zhang, "A Flexible New Technique for Camera Calibration", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330-1334, 2000 (https:/doi.org/10.1109/34.888718).
View in Google Scholar
C. Harris and M. Stephens, "A Combined Corner and Edge Detector", Proceedings of the 4th Alvey Vision Conference, UK, 1988, pp. 147-151, 1988 (https:/doi.org/10.5244/C.2.23).
View in Google Scholar
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Copyright (c) 2024 Krzysztof Klimaszewski, Tomasz Grajek, Krzysztof Wegner
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