• Chinese Journal of Quantum Electronics
  • Vol. 39, Issue 3, 364 (2022)
Renxiang MAO1、*, Jianhua CHANG1、2, Shuyi ZHANG1, Hongxu LI1, and Luyao ZHANG1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 10.3969/j.issn.1007461.2022.03.008 Cite this Article
    MAO Renxiang, CHANG Jianhua, ZHANG Shuyi, LI Hongxu, ZHANG Luyao. Multi-target real-time tracking system based on 3D-MobileNetv2[J]. Chinese Journal of Quantum Electronics, 2022, 39(3): 364 Copy Citation Text show less
    References

    [1] Zan M E, Zhou H, Han D, et al. Survey of particle filter target tracking algorithms[J]. Computer Engineering and Applications, 2019, 55(5): 8-17, 59.

    [2] Meng L, Yang X. A survey of object tracking algorithms[J]. Acta Automatica Sinica, 2019, 45(7): 1244-1260.

    [3] Li X, Zha Y F, Zhang T Z, et al. Survey of visual object tracking algorithms based on deep learning[J]. Journal of Image and Graphics, 2019, 24(12): 2057-2080.

    [4] Xiang Y, Wang Y, Zhang J C, et al. Target location estimation for vehicle dual radar based on unscented Kalman filter[J]. Opto-Electronic Engineering, 2019, 46(7): 180339.

    [5] Liu B, Zheng K K. Mean shift object tracking method based on four channel non-separable wavelets[J]. Chinese Journal of Quantum Electronics, 2018, 35(1): 13-22.

    [6] Han M, Tang X L, Wu S M, et al. Mean shift target tracking algorithm based on anisotropic kernel function[J]. Chinese Journal of Quantum Electronics, 2017, 34(2): 154-161.

    [7] Cheng Y, Li J Z, Chu L N, et al. Correlation filter tracking algorithm based on model and scale updating[J]. Laser & Optoelectronics Progress, 2018, 55(12): 121015.

    [8] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.

    [9] Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, arXiv: 1708.02002.

    [10] Lee N, Weng X S, Boddeti V N, et al. Visual compiler: Synthesizing a scene-specific pedestrian detector and pose estimator[OL]. 2016, https://arxiv.org/abs/1612.05234.

    [11] Wu D L, Xue X H, Zhang D W, et al. Multi-object detection method of 3D point cloud in outdoor scene based on Point Net++[J]. Automation & Information Engineering, 2019, 40(4): 5-10.

    [12] Lü S L, Shi D F, Hu S X. Three-dimensional imaging method of lidar based on sparse sampling technique[J]. Chinese Journal of Quantum Electronics, 2018, 35(1): 95-101.

    [13] Zhou Y, Tuzel O. VoxelNet: End-to-end learning for point cloud based 3D object detection[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 4490-4499.

    [14] Liu S X, Zheng J Y, Wang X, et al. Target detection from 3D point-cloud using Gaussian function and CNN[C]. 34rd Youth Academic Annual Conference of Chinese Association of Automation(YAC), 2019: 56267.

    [15] Ku J, Pon A D, Waslander S L. Monocular 3D object detection leveraging accurate proposals and shape reconstruction[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), 2019: 11859-11868.

    [16] Weng X S, Kitani K. Monocular 3D object detection with pseudo-LiDAR point cloud[C]. IEEE/CVF International Conference on Computer Vision Workshop(ICCVW), 2019: 857-866.

    [17] Xiang Y, Alahi A, Savarese S. Learning to track: Online multi-object tracking by decision making[C]. IEEE International Conference on Computer Vision, 2015: 4705-4713.

    [18] Wang S F, Fowlkes C C. Learning optimal parameters for multi-target tracking with contextual interactions[J]. International Journal of Computer Vision, 2017, 122(3): 48401.

    [19] Frossard D, Urtasun R. End-to-end learning of multi-senser 3D tracking by detection[C]. IEEE International Conference on Robotics and Automation, 2018: 635-642.

    [20] Simon M, Milz S, Amende K, et al. Complex-YOLO: An Euler-region-proposal for real-time 3D object detection on point clouds[OL]. 2018, arXiv: 1803.06199v2.

    [21] Weng X S, Kitani K. A baseline for 3D multi-object tracking[OL]. 2019, https://arxiv.org/abs/1907.03961.

    [22] Sandler M, Howard A, Zhu M L, et al. MobileNetV2: Inverted residuals and linear bottlenecks[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 4510-4520.

    [23] Meng L, Xu L, Guo J Y. Semantic segmentation algorithm based on improved Mobile NetV2[J]. Acta Electronica Sinica, 2020, 48(9): 1769-1776.

    MAO Renxiang, CHANG Jianhua, ZHANG Shuyi, LI Hongxu, ZHANG Luyao. Multi-target real-time tracking system based on 3D-MobileNetv2[J]. Chinese Journal of Quantum Electronics, 2022, 39(3): 364
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