Laser & Optoelectronics Progress, Volume. 59, Issue 12, 1215003(2022)
Target Tracking Algorithm Based on Siamese Network of Feature Optimization Model
Figures & Tables(17)
Fig. 6. Comparison of test results of various algorithms in OTB100 data set. (a) Precision rate; (b) success rate
Table 1. Structure of backbone network
View tableTable 1. Structure of backbone network
Layer number Network structure Convolution kernels Stride Channel
number
Template image /pixel Search image /pixel Layer1 Input layer ‒3 135×135 263×263 Conv2d 3 1 96‒3 133×133 261×261 Conv2d 3 1 96‒96 131×131 259×259 MaxPool2d 3 2 ‒ 65×65 129×129 Layer2 Conv2d 3 1 128‒96 63×63 127×127 Conv2d 3 1 128‒128 61×61 125×125 MaxPool2d 3 2 ‒ 30×30 62×62 Layer3 Conv2d 3 1 256‒128 28×28 60×60 Conv2d 3 1 256‒256 26×26 58×58 Conv2d 3 1 256‒256 24×24 56×56 MaxPool2d 2 2 ‒ 12×12 28×28 Layer4 Conv2d 3 1 512‒256 10×10 26×26 Layer5 Conv2d 3 1 512‒512 8×8 24×24
Table 2. Comparison of experimental data
View tableTable 2. Comparison of experimental data
Name Epoch Got-10k ILSVR2015_VID Precision rate Success rate Speed/（frame·s-1） SCSAtt 50 √ √ 0.855 0.641 59.871 Proposed 20 √ √ 0.853 0.648 59.497
Table 3. Comparison of test results of various algorithms in VOT2018 data set
View tableTable 3. Comparison of test results of various algorithms in VOT2018 data set
Name Accuracy EAO Speed /（frame·s-1） Proposed 0.5360 0.1920 44.33 SiamFC 0.4943 0.1875 31.89 LSART 0.4932 0.3230 1.00 CSRDCF 0.4910 0.2562 10.20 DeepSRDCF 0.4896 0.2753 65.30 ECO-HC 0.4842 0.2486 75.60
Table 4. Challenge performance results of various algorithms in OTB100 data set
View tableTable 4. Challenge performance results of various algorithms in OTB100 data set
Name IPR IV BC OCC DEF SV LR FM OPR OV MB Proposed Suc 0.624 0.646 0.609 0.613 0.609 0.636 0.682 0.616 0.630 0.545 0.628 Pre 0.842 0.844 0.808 0.807 0.831 0.846 0.998 0.797 0.854 0.715 0.800 Siam
RPN
Suc 0.628 0.649 0.591 0.585 0.617 0.615 0.639 0.599 0.625 0.542 0.622 Pre 0.854 0.859 0.799 0.780 0.825 0.838 0.978 0.789 0.851 0.726 0.816 Siam
DWfc
Suc 0.606 0.622 0.574 0.601 0.560 0.613 0.596 0.630 0.612 0.590 0.654 Pre 0.824 0.794 0.762 0.798 0.763 0.819 0.901 0.808 0.829 0.781 0.841 CFNet Suc 0.567 0.541 0.561 0.527 0.526 0.546 0.614 0.554 0.553 0.454 0.540 Pre 0.786 0.707 0.756 0.699 0.714 0.731 0.888 0.705 0.759 0.601 0.680 Siam
FC
Suc 0.559 0.572 0.527 0.549 0.512 0.556 0.618 0.571 0.561 0.509 0.554 Pre 0.743 0.736 0.692 0.723 0.691 0.736 0.900 0.744 0.758 0.673 0.707 Staple Suc 0.548 0.529 0.560 0.543 0.551 0.521 0.394 0.540 0.533 0.475 0.541 Pre 0.768 0.783 0.749 0.726 0.752 0.726 0.690 0.708 0.737 0.664 0.698 SRDCF Suc 0.544 0.613 0.583 0.559 0.544 0.561 0.514 0.597 0.550 0.460 0.594 Pre 0.745 0.792 0.775 0.734 0.734 0.745 0.760 0.768 0.741 0.594 0.765 fDSST Suc 0.505 0.559 0.523 0.460 0.427 0.475 0.382 0.458 0.477 0.386 0.469 Pre 0.698 0.722 0.704 0.602 0.550 0.648 0.678 0.570 0.654 0.474 0.566
Table 5. Overall data of deep network ablation experiment
View tableTable 5. Overall data of deep network ablation experiment
Name Improved VGG-Net Improved Hourglass AlexNet Precision rate Success rate Proposed-2 √ √ - 0.560 0.724 Proposed-A - √ √ 0.538 0.703
Table 6. Experimental data of OTB100 challenge ablation in deep network
View tableTable 6. Experimental data of OTB100 challenge ablation in deep network
Name IPR IV BC OCC DEF SV LR FM OPR OV MB Proposed-2 Suc 0.522 0.510 0.479 0.501 0.490 0.543 0.604 0.559 0.529 0.439 0.544 Pre 0.677 0.642 0.619 0.643 0.647 0.709 0.872 0.695 0.700 0.569 0.664 Proposed-A Suc 0.511 0.480 0.458 0.487 0.462 0.520 0.554 0.536 0.522 0.431 0.532 Pre 0.659 0.608 0.614 0.624 0.622 0.686 0.812 0.673 0.691 0.571 0.663
Table 7. Ablation experiment data
View tableTable 7. Ablation experiment data
Name Improved VGG-Net Improved Hourglass Hourglass Layer Precision rate Success rate SCSAtt √ - - - 0.687 0.529 Proposed-1 √ √ - 1 0.698 0.538 Proposed-2 √ √ - 2 0.724 0.560 Proposed-3 √ √ - 3 0.704 0.539 Proposed-No √ - √ 2 0.694 0.530
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Yongqiang Wu, Baohua Zhang, Xiaoqi Lv, Yu Gu, Yueming Wang, Xin Liu, Yan Ren, Jianjun Li, Ming Zhang. Target Tracking Algorithm Based on Siamese Network of Feature Optimization Model[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1215003
Paper Information
Category: Machine Vision
Received: Apr. 25, 2021
Accepted: Jun. 2, 2021
Published Online: May. 23, 2022
The Author Email: Baohua Zhang (zbh_wj2004@imust.cn)
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