Laser & Optoelectronics Progress, Volume. 59, Issue 22, 2215010(2022)
Multiobject Tracking Algorithm Combining YOLO-V4 and Improved SiameseRPN
Figures & Tables(13)
Fig. 5. Performance indicator ranking on VOT2016. (a) Accuracy-robustness; (b) average overlap expectation
Fig. 6. Performance indicator ranking on VOT2018. (a) Accuracy-robustness; (b) average overlap expectation
Fig. 7. Comparison of tracking results in different scenarios. (a) Out-of-view; (b) background clutters; (c) partial occlusion; (d) scale variation; (e) deformation
Table 1. Siamese network structure incorporating residual connections
View tableTable 1. Siamese network structure incorporating residual connections
Convolution layer Convolution kernel Output and input channel Stride Template image Search image Channel 127×127 255×255 3 Conv1 3×3 64×3 1 125×125 253×253 64 Res1 1×13×3 64×64 1 123×123 251×251 32
64
Maxpool1 2×2 2 61×61 125×125 64 Res2 1×13×3 128×64 1 59×59 123×123 64128 Res3 1×13×3 128×128 1 57×57 121×121 64128 Maxpool2 2×2 2 28×28 60×60 128 Res4 1×13×3 256×128 1 26×26 58×58 128256 Res5 1×13×3 256×256 1 24×24 56×56 128256 Maxpool3 2×2 2 12×12 28×28 256 Res6 1×13×3 256×256 1 10×10 26×26 128256 Res7 1×13×3 512×256 1 8×8 24×24 256512 Conv2 1×1 256×512 1 8×8 24×24 256 Conv3 3×3 256×256 1 6×6 22×22 256
Table 2. Comparison results of different algorithms on VOT2016 dataset
View tableTable 2. Comparison results of different algorithms on VOT2016 dataset
Algorithm Acc Rob EAO SiameseFC 0.5342 0.4613 0.2356 Staple 0.5425 0.3784 0.2946 SRDCF 0.5356 0.4193 0.2459 DeepSRDCF 0.5271 0.3264 0.2758 TADT 0.5539 0.3327 0.2991 SiameseRPN 0.5617 0.2621 0.3442 Proposed algorithm 0.5808 0.1561 0.4095
Table 3. Comparison results of different algorithms on VOT2018 dataset
View tableTable 3. Comparison results of different algorithms on VOT2018 dataset
Algorithm Acc Rob EAO SiameseFC 0.5108 0.4836 0.2343 Staple 0.5246 0.6887 0.1694 DSiamese 0.5117 0.6458 0.1966 SiameseDW 0.5411 0.4032 0.2704 CFNet 0.5028 0.5853 0.1882 SiameseRPN 0.4945 0.4627 0.2441 Proposed algorithm 0.5347 0.3417 0.3091
Table 4. Experimental results of ablation on OTB100 dataset
View tableTable 4. Experimental results of ablation on OTB100 dataset
Algorithm AUC Prec SiameseRPN 0.596 0.785 SiameseRPN+RC 0.638 0.822 SiameseRPN+AB 0.609 0.804 SiameseRPN+RC+AB 0.654 0.846
Table 5. Comparison of Prec of different video attributes on OTB100 for each algorithm
View tableTable 5. Comparison of Prec of different video attributes on OTB100 for each algorithm
Attribute Number of videos SiameseFC Staple SRDCF ACFN SiameseRPN Proposed algorithm SV 63 0.765 0.721 0.739 0.758 0.806 0.841 OCC 48 0.738 0.729 0.730 0.752 0.781 0.837 DEF 43 0.779 0.742 0.725 0.764 0.793 0.843 OV 14 0.695 0.670 0.593 0.687 0.736 0.780 BC 31 0.703 0.747 0.767 0.766 0.803 0.824
Table 6. Performance comparison of each algorithm
View tableTable 6. Performance comparison of each algorithm
Algorithm MOTP MOTA FPS SORT 0.814 0.815 112 DeepSort 0.817 0.869 48 MHT 0.822 0.871 1.5 POI 0.835 0.883 15 SiameseCNN 0.764 0.865 107 Proposed algorithm 0.891 0.937 39
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Zhiling Zhu, Zhifeng Zhou, Yong Zhao, Yongquan Wang, Liduan Wang. Multiobject Tracking Algorithm Combining YOLO-V4 and Improved SiameseRPN[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2215010
Paper Information
Category: Machine Vision
Received: Aug. 19, 2021
Accepted: Oct. 19, 2021
Published Online: Oct. 13, 2022
The Author Email: Zhifeng Zhou (zhousjtu@126.com)
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