Abstract
In the Internet of Vehicles, the binocular stereo vision ranging method has the advantages of high efficiency, simple system structure and low cost, and its ranging function is an indispensable part of the intelligent network vehicle terminal, but how to use it to determine the ranging range is rarely studied. To solve the problem that the binocular stereo vision plane-space algorithm cannot evaluate the distance range on the limited surface, this paper proposes a surface constraint-range sweep algorithm, which is applied to evaluate the distance range between pedestrians and vehicles in Internet of Vehicles environment. First, according to the image sensor information of the ranging model, the model parameters of the plane-space algorithm are calculated. Next, whether the projection of the target point is on the image sensor is calculated according to the principle of geometric optical imaging. For the point projected on the image sensor, the error margin of the algorithm point can be set according to the working environment. If the error margin is met, the measurement point is considered to be within the limited surface value range. Finally, according to the actual object, the point cloud of the target surface is obtained by using the geometric optics theory, and the range of the plane-space algorithm on the limited surface is calculated, so as to construct the ranging evaluation model of the plane-space algorithm. The experimental results show that the efficiency of the algorithm can reach 99. 8% by adjusting the parameters of the surface constraint-range sweep algorithm. The surface constraint-range sweep algorithm can more accurately evaluate the range of the plane-space algorithm on the limited surface.
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References
Wang, Y., Zhao, Y.: Research on service monitoring system of intelligent connected vehicle based on cloud platform. In: 2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS), pp. 963–966. IEEE (2021)
Geiger, D., Ladendorf, B., Yuille, A.: Occlusions and binocular stereo. Int. J. Comput. Vision 14(3), 211–226 (1995)
Sun, X., Jiang, Y., Ji, Y., et al.: Distance measurement system based on binocular stereo vision. IOP Conf. Ser.: Earth Environ. Sci. IOP Publish. 252(5), 052051 (2019)
Wang, C., Zou, X., Tang, Y., et al.: Localisation of litchi in an unstructured environment using binocular stereo vision. Biosys. Eng. 145, 39–51 (2016)
Shuai, G., Wenlun, M., Jingjing, F., et al.: Target recognition and range-measuring method based on binocular stereo vision. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI), pp. 623–626. IEEE (2020)
Guo, S., Chen, S., Liu, F., et al.: Binocular vision-based underwater ranging methods. In: 2017 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 1058–1063. IEEE (2017)
Zhang, Q., Ma, C.: Automobile braking performance detection system based on binocular stereo vision. J. Phys.: Conf. Ser. IOP Publishing 2037(1), 012082 (2021)
Mu, Q., Wei, J., Yuan, Z., et al.: Research on target ranging method based on binocular stereo vision. In: 2021 International Conference on Intelligent Computing, Automation and Applications (ICAA), pp. 81–85. IEEE (2021)
Marr, D., Poggio, T.: A computational theory of human stereo vision. Proc. R. Soc. Lond. 204(1156), 301–328 (1979)
Liu, Z., Chen, T.: Distance measurement system based on binocular stereo vision. International Joint Conference on Artificial Intelligence. IEEE (2009)
Ye, Q., Cheng, Y., Zhang, M., et al.: Research on flame location and distance measurement method based on binocular stereo vision. In: 2020 Chinese Automation Congress (CAC), pp. 4089–4094. IEEE (2020)
Zhang, J., Hu, S., Shi, H.: Deep learning based object distance measurement method for binocular stereo vision blind area. Methods, 9(9) (2018)
Zaarane, A., Slimani, I., Al Okaishi, W., et al.: Distance measurement system for autonomous vehicles using stereo camera. Array 5, 100016 (2020)
Li, J., Wu, J., You, Y., et al.: Parallel binocular stereo-vision-based GPU accelerated pedestrian detection and distance computation. J. Real-Time Image Proc. 17(3), 447–457 (2020)
Zhang, E., Wang, S., Sun, Y.: A new binocular stereovision measurement by using plane-space algorithm. In: Liang, Q., Jiasong, Mu., Jia, M., Wang, W., Feng, X., Zhang, B. (eds.) Communications, Signal Processing, and Systems: Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems, pp. 622–628. Springer Singapore, Singapore (2019). https://doi.org/10.1007/978-981-10-6571-2_76
Acknowledgment
Shubin Wang (wangshubin@imu.edu.cn) is the correspondent author and this work was supported by the National Natural Science Foundation of China (61761034), the Natural Science Foundation of Inner Mongolia (2020MS06022).
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Shi, Q., Zhang, X., Zhu, H., Li, X., Wang, S. (2023). Research on Ranging Range Based on Binocular Stereo Vision Plane-Space Algorithm. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2022. Lecture Notes in Electrical Engineering, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-99-1260-5_7
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DOI: https://doi.org/10.1007/978-981-99-1260-5_7
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