Infrared and Laser Engineering, Volume. 51, Issue 10, 20220042(2022)
A survey of siamese networks tracking algorithm integrating detection technology
Figures & Tables(10)
Fig. 1. The relation and difference between object detection and object tracking
Fig. 8. Boundaries with uncertainty. (a) Non-rigid deformation; (b) Occlusion; (c) Motion blur
Table 1. Performance comparison of siamese tracking methods on OTB100, LaSOT, GOT-10 k and VOT2018
View tableView in ArticleTable 1. Performance comparison of siamese tracking methods on OTB100, LaSOT, GOT-10 k and VOT2018
TYPE OTB100 LaSOT GOT10 k VOT2018 A S AUC PR AUC. NPR AO SR0.50 SR0.75 A R EAO Note: Bold fontsare ranked top-3. '-' means the corresponding results are not given in the original literature. 'TYPE' is the classification basis delineated in this paper, where 'A' indicates the Anchor (Anchor-based 'T '/Anchor-free 'F'), 'S' indicates the Stage number (One-stage '1'/Two-stages '2 '), and 'others' indicates other classes. SiamRPN [ 29 ]T 1 0.637 0.851 0.457 - - - - - - - DaSiamRPN [ 35 ]T 1 0.658 0.88 0.415 0.496 - - - 0.59 0.276 0.383 SiamRPN++ [ 38 ]T 1 0.696 0.915 0.496 0.569 0.518 0.618 0.325 0.6 0.234 0.414 SiamDW [ 37 ]T 1 0.674 0.923 0.384 0.476 0.416 - - - - 0.27 SiamMask [ 31 ]T 1 - - - - 0.514 0.587 0.366 0.61 0.276 0.38 SiamMan [ 33 ]T 1 0.705 0.919 - - - - - 0.605 0.183 0.462 THOR [ 54 ]T 1 0.648 0.791 - - 0.447 0.538 0.204 0.582 0.234 0.416 DROL [ 58 ]T 1 0.715 0.934 0.537 0.624 - - - 0.616 - 0.481 SiamAttn [ 55 ]T 1 0.712 0.926 0.56 0.648 - - - 0.636 0.16 0.47 AFAT [ 56 ]T 1 0.663 0.874 0.492 0.574 - - - 0.605 0.239 0.419 UpdateNet [ 57 ]T 1 - - 0.475 0.56 - - - - - 0.393 SiamFC++ [ 41 ]F 1 0.683 0.896 0.544 0.623 0.595 0.695 0.479 0.587 0.183 0.426 AFSN [ 49 ]F 1 0.675 0.868 - - - - - 0.589 0.204 0.398 SATIN [ 48 ]F 1 0.641 0.844 - - - - - - - - SiamBAN [ 43 ]F 1 0.696 0.91 0.514 0.598 - - - 0.597 0.178 0.452 SiamCAR [ 40 ]F 1 0.697 0.91 - - 0.569 0.67 0.415 - - - CGACD [ 50 ]F 1 0.713 0.922 0.518 0.626 - - - 0.615 0.173 0.449 FCAF [ 80 ]F 1 0.649 0.86 - - - - - - - - FCOT [ 45 ]F 1 0.693 0.913 0.569 0.678 0.64 0.763 0.517 0.6 0.108 0.508 PGNet [ 34 ]F 1 0.691 0.892 0.531 0.605 - - - 0.618 0.192 0.447 Ocean [ 19 ]F 1 0.684 0.92 0.56 - 0.611 0.721 0.473 0.592 0.117 0.489 Ocean+ [ 44 ]F 1 - - - - - - - - - - RPT [ 52 ]F 0.715 0.936 - - 0.624 0.73 0.504 0.629 0.103 0.51 AlphaRef [ 51 ]1 - - 0.589 0.649 - - - 0.633 0.136 0.476 SiamKPN [ 64 ]F 2 0.712 0.927 0.498 - 0.529 0.606 0.362 0.606 0.192 0.44 SPLT [ 61 ]T 2 - - 0.426 0.494 - - - - - - CRPN [ 63 ]T 2 0.663 - 0.455 0.542 - - - - - - SPM [ 59 ]T 2 0.687 0.889 0.485 - 0.513 0.593 0.359 0.58 0.3 0.338 TACT [ 67 ]T 2 - - 0.575 0.66 0.578 0.665 0.477 - - - SiamRCNN [ 69 ]T 2 0.701 0.891 0.648 0.722 0.649 0.728 0.597 0.609 0.22 0.408 GlobalT [ 68 ]T 2 - - 0.521 0.599 - - - - - - LTAO [ 70 ]T 2 - - - - - - - - - - ATOM [ 72 ]others 0.667 0.879 0.514 0.576 0.556 0.635 0.402 0.59 0.204 0.401 DiMP [ 65 ]others 0.686 0.899 0.569 0.648 0.611 0.717 0.492 0.597 0.153 0.44 PrDiMP [ 66 ]others 0.696 0.897 0.598 - 0.634 0.738 0.543 0.618 0.165 0.442 SSD-MAML [ 71 ]others 0.62 - - - - - - - - - FRCNN-MAML [ 71 ]others 0.647 - - - - - - - - - FCOS-MAML [ 21 ]others 0.704 0.905 0.523 - - - - 0.635 0.22 0.392 Retina-MAML [ 21 ]others 0.712 0.926 0.48 - - - - 0.604 0.159 0.452
Table 2. Comparison of advantages and disadvantages of siamese trackers with different detection techniques
View tableView in ArticleTable 2. Comparison of advantages and disadvantages of siamese trackers with different detection techniques
Taxonomy Advantage limitation State estimation Anchor-based First Introducing RPN detection technology; Discarding multi-scale search, and can predict bbox with arbitrary aspect ratio Relying on prior knowledge; Incapable of rectifying weak prediction Anchor-free Fewer parameters and faster speed; Correcting weak predictions caused by deformation and fast movement Requiring additional constraints (such as location quality) due to the lack of prior knowledge Stage number One-stage Fast speed;； Easy to add additional modules (e.g. model updates) Weak discriminability for semantic interference Two-stage Better balance of robustness and discriminability Complex structure and slow speed Others IOUNet-based prediction More accurate evaluation of location quality - Detector transform tracker Narrowing the differences between detection and tracking with a common pattern to solve both problems -
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Jinpu Zhang, Yuehuan Wang. A survey of siamese networks tracking algorithm integrating detection technology[J]. Infrared and Laser Engineering, 2022, 51(10): 20220042
Paper Information
Category: Image processing
Received: Jan. 13, 2022
Accepted: --
Published Online: Jan. 6, 2023
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