Abstract
Intelligentization of car navigation is an inevitable trend. Visual navigation has the advantages of high precision in short distances and low cost. This paper proposes a fuzzy control reversing system based on visual information. We obtain the trajectory of the rear camera by constructing reversing model of the car. YOLO (You Only Look Once) is used to detect pedestrians and cars appearing in the camera field of view and segment the detected images during the reversing process. The dynamic feature points are removed effectively by the proposed environmental statistical information analysis method. Using visual information to construct constraints to improve the traditional fuzzy control reversing system can provide drivers with accurate driving assistance information and effectively reduce the probability of accidents such as collisions. The experimental results show that the proposed method is effective and feasible.
This work is supported by the open fund of Shaanxi Key Laboratory of Integrated and Intelligent Navigation SKLIIN-20180102 and SKLIIN-20180107. This is a student work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adams, M., Vo, B., Mahler, R., Mullane, J.: SLAM gets a PHD: new concepts in map estimation. IEEE Robot. Autom. Mag. 21(2), 26–37 (2014)
Häne, C., et al.: 3D visual perception for self-driving cars using a multi-camera system: calibration, mapping, localization, and obstacle detection. Image Vis. Comput. S0262885617301117 (2017)
Hee Lee, G., Faundorfer, F., Pollefeys, M.: Motion estimation for self-driving cars with a generalized camera. In: Computer Vision and Pattern Recognition (2013)
Halgamuge, S.K., Runkler, T.A., Glesner, M.: A hierarchical hybrid fuzzy controller for real-time reverse driving support of vehicles with long trailers. In: IEEE Conference on Fuzzy Systems, IEEE World Congress on Computational Intelligence (1994)
Chen, G., Zhang, D.: Back-driving a truck with suboptimal distance trajectories: a fuzzy logic control approach. IEEE Trans. Fuzzy Syst. 5(3), 369–380 (1997)
Nogami, S., Hidaka, K.: A stereo camera based static and moving obstacles detection on autonomous visual navigation of indoor transportation vehicle. In: IECON 2018–44th Annual Conference of the IEEE Industrial Electronics Society, pp. 5421–5426, October 2018
Alvarez, H., Paz, L.M., Sturm, J., Cremers, D.: Collision avoidance for quadrotors with a monocular camera. In: Hsieh, M.A., Khatib, O., Kumar, V. (eds.) Experimental Robotics. STAR, vol. 109, pp. 195–209. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-23778-7_14
He, K., Gkioxari, G., Dollar, P., Girshick, R: Mask R-CNN. IEEE Trans. Pattern Anal. Mach. Intell. (2017)
Bescos, B., Fácil, J.M., Civera, J., Neira, J.: DynaSLAM: tracking, mapping, and inpainting in dynamic scenes. IEEE Robot. Autom. Lett. 3(4), 4076–4083 (2018)
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Yu, C., et al.: DS-SLAM: a semantic visual slam towards dynamic environments. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1168–1174 (2018)
Rünz, M., Buffier, M., Agapito, L.: MaskFusion: real-time recognition, tracking and reconstruction of multiple moving objects. In: 2018 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 10–20 (2018)
Camurri, M., Bazeille, S.: Real-time depth and inertial fusion for local slam on dynamic legged robots. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (2015)
Rezaei, M., Terauchi, M., Klette, R.: Robust vehicle detection and distance estimation under challenging lighting conditions. IEEE Trans. Intell. Transp. Syst. 16(5), 2723–2743 (2015)
Li, B., Zhang, X., Sato, M.: Pitch angle estimation using a vehicle-mounted monocular camera for range measurement. In: 2014 12th International Conference on Signal Processing (ICSP), pp. 1161–1168, October 2014
Kim, H., Lee, Y., Woo, T., Kim, H.: Integration of vehicle and lane detection for forward collision warning system. In: IEEE International Conference on Consumer Electronics-Berlin (2016)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32
Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. CoRR, abs/1804.02767 (2018)
Danping, Z., Ping, T.: CoSLAM: collaborative visual SLAM in dynamic environments. IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 354–366 (2013)
Fan, Y., Han, H., Tang, Y., Zhi, T.: Dynamic objects elimination in SLAM based on image fusion. Pattern Recognit. Lett. (2018)
Bayona, A., SanMiguel, J.C., Martínez, J.M.: Stationary foreground detection using background subtraction and temporal difference in video surveillance. In IEEE International Conference on Image Processing (2010)
Kong, S.G., Kosko, B.: Adaptive fuzzy systems for backing up a truck-and-trailer. IEEE Trans. Neural Netw. 3(2), 211–223 (1992)
Kosko, B.: Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence, January 1992
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, S., Fan, Y., Tang, Y., Jing, X., Yao, J., Han, H. (2019). Fuzzy Control Reversing System Based on Visual Information. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11859. Springer, Cham. https://doi.org/10.1007/978-3-030-31726-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-31726-3_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31725-6
Online ISBN: 978-3-030-31726-3
eBook Packages: Computer ScienceComputer Science (R0)