• Infrared and Laser Engineering
  • Vol. 52, Issue 4, 20220506 (2023)
Jie Feng1,2, Yang Feng3, Xiang Liu1, Chenjin Deng2,4, and Zhongjun Yu1,2
Author Affiliations
  • 1Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100194, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3School of Software Engineering, South China University of Technology, Guangzhou 510006, China
  • 4Key Laboratory of Quantum Optics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
  • show less
    DOI: 10.3788/IRLA20220506 Cite this Article
    Jie Feng, Yang Feng, Xiang Liu, Chenjin Deng, Zhongjun Yu. Fast detection of moving targets in long range surveillance LiDAR[J]. Infrared and Laser Engineering, 2023, 52(4): 20220506 Copy Citation Text show less
    References

    [1] Hao Deng, Wei Zheng, Mingtao Li, et al. Dim moving target detection based on fluctuation analysis. Optics and Precision Engineering, 28, 2517-2526(2020).

    [2] Dezhen Yang, Songlin Yu, Jinjun Feng, et al. Low false alarm infrared target detection in airborne complex scenes. Optics and Precision Engineering, 30, 96-107(2022).

    [3] Shanshan Song, Xuping Zhai. Improved infrared anomaly target detection algorithm based on single Gaussian model. Infrared Technology, 43, 885-888, 894(2021).

    [4] Rong Zhen, Ziqiang Shi. Ship trajectory clustering method based on Gaussian mixture model. Ship Engineering, 43, 139-143(2021).

    [5] Min’an Tang, Chenyu Wang. Moving object detection in static scene base on improved vibe algorithm. Laser & Opto-electronics Progress, 58, 1410011(2021).

    [6] L Liu, G H Chai, Z Qu. Moving target detection based on improved ghost suppression and adaptive visual background extraction. Journal of Central South University, 28, 747-759(2021).

    [7] Peng Sun, Yue Yu, Jiaxin Chen, et al. Highly dynamic aerial polymorphic target detection method based on deep spatial-temporal feature fusion (Invited). Infrared and Laser Engineering, 51, 20220167(2022).

    [8] Yang H Z. Research on real time target clustering recognition of LiDAR point cloud f autonomous driving[D]. Hefei: University of Science Technology of China, 2021. (in Chinese)

    [9] Yuan H N, Sun W, Xiang T Y. Line laser point cloud segmentation based on the combination of RANSAC region growing[C]Proceedings of the 39th Chinese Control Conference, 2020: 63246328.

    [10] Maturana D, Scherer S. Vox: A 3D Convolutional Neural wk f realtime object recognition[C]International Conference on Intelligent Robots Systems(IROS). IEEE, 2015: 922928.

    [11] Qi C R, Yi L, Su H, et al. Point++: Deep hierarchical feature learning on point sets in a metric space[C]Advances in Neural Infmation Processing Systems, 2017: 50995108.

    [12] Ruiyan Zhang, Xiujie Jiang, Junshe An, et al. Design of global-contextual detection model for optical remote sensing targets. Chinese Optics, 13, 1302-1313(2020).

    [13] Shi S H, Wang X G , Li H S. PointRCNN: 3D object proposal generation detection from point cloud[C]Proceedings of the IEEE Conference on Computer Vision Pattern Recognition (CVPR), Long Beach: IEEE, 2019: 770779.

    [14] S Q Ren, K M He, R Girshick, et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

    [15] Redmon J, Farhadi A. YOLOv3: An Incremental Improvement[EBOL]. (20180408)[2022−10−09]. https:arxiv.gabs1804.02767.

    [16] Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detect[C]European Conference on Computer Vision (ECCV). Amsterdam, The herls: Springer, 2016: 2137.

    [17] Yong Bi, Mingqi Pan, Shuo Zhang, et al. Overview of 3D point cloud super-resolution technology. Chinese Optics, 15, 210-223(2022).

    [18] Geiger A, Lenz P, Urtasun R. Are we ready f autonomous driving The KITTI Vision benchmark suite[C]IEEE Conference on Computer Vision & Pattern Recognition (CVPR), Providence, RI, USA. New Yk: IEEE, 2012: 33543361.

    [19] A Geiger, P Lenz, C Stiller, et al. Vision meets robotics: The KITTI Dataset. International Journal of Robotics Research, 32, 1231-1237(2013).

    [20] Ye Shi, Xiaokai Wang, Huifeng Liu. Research on bilateral filtering algorithm base on local filtering template. Journal of Test and Measurement Technology, 35, 49-53(2021).

    [21] Chengbin Xing, Shengsheng Gong, Xiaoliang Yu, et al. Application of Gaussian mixture clustering to moving surface fitting filter classification. Infrared and Laser Engineering, 50, 20200501(2021).

    [22] R Nitzberg. Clutter map CFAR analysisl. IEEE Transactions on Aerospace and Electronic Systems, 22, 419-421(1986).

    [23] Chao Ban, Weilin Pan, Rui Wang, et al. Initial results of Rayleigh scattering lidar observations at Zhongshan station, Antarctica. Infrared and Laser Engineering, 50, 20210010(2021).

    Jie Feng, Yang Feng, Xiang Liu, Chenjin Deng, Zhongjun Yu. Fast detection of moving targets in long range surveillance LiDAR[J]. Infrared and Laser Engineering, 2023, 52(4): 20220506
    Download Citation