Abstract
Vehicle forward target recognition is the most concerned part in the field of environmental perception. In order to overcome the limitation of single sensor in target recognition, this paper proposes a forward target perception algorithm based on fusion of camera and millimeter wave (MMW) radar. Considering the characteristics of object and sensor, this paper divides vehicle forward targets into two categories: close-range target and distant target. For the close-range target, the target information obtained by the two sensors is matched and fused at the data level by using object recognition, monocular vision ranging, Kalman filter and other algorithms. For the distant target, the initial position is determined by the radar detection point, and the target is accurately classified by the visual algorithm. Experimental results show that the proposed algorithm can effectively reduce the rate of missed detection and improve the target stable recognition distance to 90 m. Besides, more accurate and abundant target information can be obtained by this method.
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References
Chavez-Garcia RO, Vu T-D, Aycard O (2014) Fusion at detection level for frontal object perception. In: Intelligent vehicles symposium proceedings 2014. IEEE
Alencar FARR, Rosero LA, Filho CM, Osorio FS, Wolf DF (2015) Fast metric tracking by detection system: radar blob and camera fusion. In: 2015 12th Latin American robotics symposium and 2015 3rd Brazilian symposium on robotics (LARS-SBR). IEEE
Xiao W, Xu L, Sun H, Xin J, Zheng N (2016) On-road vehicle detection and tracking using MMW radar and monovision fusion. IEEE Trans Intell Transp Syst 17:2075–2084
Siyang H, Xiao W, Linhai X, Hongbin S, Nanning Z (2016) Frontal object perception for intelligent vehicles based on radar and camera fusion. In: Control Conference. IEEE
Guangyao Z et al (2017) Tramway obstacles detection based on information fusion of MMV radar and machine vision. Chin J Internet Things 1(02):76–83
Zhengyou Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11):1330–1334
Heikkila J, Silven O (1997) A four-step camera calibration procedure with implicit image correction. In: Computer vision pattern recognition, CVPR. IEEE Computer Society, pp 1106–1112
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: single shot multibox detector. In: European conference on computer vision, pp 21–37
Lei G et al (2006) Study on realtime distance detection based on monocular vision technique. J Image Graph 11(1):74–81
Bar-Shalom Y (1987) Tracking and data association. Academic Press Professional, Inc
Acknowledgments
This work was supported by the National Key Research and Development Program of China (2016YFB0101001) and the Beijing Municipal Science and Technology Project under Grant # Z181100008918003. The authors would also like to thank the insightful and constructive comments from anonymous reviewers.
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Yu, G., Zhang, S., Niu, H., Zhou, B., Liu, G., Li, D. (2020). Research on Vehicle Forward Target Recognition Algorithm Based on Vision and MMW Radar Fusion. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 594. Springer, Singapore. https://doi.org/10.1007/978-981-32-9698-5_58
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DOI: https://doi.org/10.1007/978-981-32-9698-5_58
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