• Laser & Optoelectronics Progress
  • Vol. 57, Issue 24, 241008 (2020)
Dianwei Wang1、*, Haoyu Fang1、*, Ying Liu1, Jing Jiang1, Xincheng Ren2, Zhijie Xu3, and Yongrui Qin3
Author Affiliations
  • 1School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China
  • 2School of Physics and Electronic Information, Yan'an University, Yan'an, Shaanxi 716000, China
  • 3School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH UK
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    DOI: 10.3788/LOP57.241008 Cite this Article Set citation alerts
    Dianwei Wang, Haoyu Fang, Ying Liu, Jing Jiang, Xincheng Ren, Zhijie Xu, Yongrui Qin. Algorithm for Panoramic Video Tracking Based on Improved SiameseRPN[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241008 Copy Citation Text show less
    Scale change of target motion in panoramic video
    Fig. 1. Scale change of target motion in panoramic video
    Panoramic image stitched by seven cameras
    Fig. 2. Panoramic image stitched by seven cameras
    Visualization results of deep features in convolutional neural networks. (a) Conv 1; (b) Relu 1; (c) Pool 1; (d) Conv 3; (e) Relu 3; (f) Pool 3; (g) Conv 5; (h) Relu 5; (i) original image
    Fig. 3. Visualization results of deep features in convolutional neural networks. (a) Conv 1; (b) Relu 1; (c) Pool 1; (d) Conv 3; (e) Relu 3; (f) Pool 3; (g) Conv 5; (h) Relu 5; (i) original image
    Network architecture of proposed algorithm
    Fig. 4. Network architecture of proposed algorithm
    Convolution structure in MobileNetV3
    Fig. 5. Convolution structure in MobileNetV3
    Comparison among Relu6, h-swish, and swish activation functions
    Fig. 6. Comparison among Relu6, h-swish, and swish activation functions
    Curve of loss function
    Fig. 7. Curve of loss function
    Comparison of experiment results by SiameseRPN and improved network
    Fig. 8. Comparison of experiment results by SiameseRPN and improved network
    Comparison of results by different algorithms in four different scenarios
    Fig. 9. Comparison of results by different algorithms in four different scenarios
    Experimental results for small targets and target occlusion
    Fig. 10. Experimental results for small targets and target occlusion
    Experimental results for multi-target cross movements
    Fig. 11. Experimental results for multi-target cross movements
    Experimental results for similar target interference
    Fig. 12. Experimental results for similar target interference
    Test results of six algorithms on panoramic dataset. (a) Precision; (b) success rate
    Fig. 13. Test results of six algorithms on panoramic dataset. (a) Precision; (b) success rate
    ClassificationTotal videoBeing blockedSmall objectSimilar targetUnderexposureScale change
    Car303042312
    Person2776619
    Motor1210198
    Bicycle632033
    Table 1. Distribution of tracking difficulties in panoramic data sets
    PerformanceMDNetADNetRT-MDNetSiameseRPNSiameseRPN++Ours
    Precision /%0.7440.3160.8010.7830.8330.896
    Success rate /%0.5620.6730.5160.7310.7570.855
    Speed /(frame·s-1)148759133
    Table 2. Performance comparison of all algorithms
    Dianwei Wang, Haoyu Fang, Ying Liu, Jing Jiang, Xincheng Ren, Zhijie Xu, Yongrui Qin. Algorithm for Panoramic Video Tracking Based on Improved SiameseRPN[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241008
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