| Trackers | Debug | Train | Test | Evaluation | Toolkit | GPU | Version | Upload |
|---|---|---|---|---|---|---|---|---|
| Siamese | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||
| SiamFC | ✔ | ✔ | ✔ | ✔ | got10k | ✔ | unofficial | ✔ |
| SiamRPN | ✔ | ✔ | ✔ | ✔ | got10k | ✔ | unofficial | ✔ |
| DaSiamRPN | ✔ | ✔ | ✔ | pysot | ✔ | unofficial | ✔ | |
| UpdateNet | ✔ | ✔ | ✔ | ✔ | pysot | ✔ | unofficial | ✔ |
| SiamDW | ✔ | ✔ | ✔ | ✔ | - | ✔ | official | |
| SiamRPN++ | ✔ | ✔ | ✔ | ✔ | pysot | ✔ | official | ✔ |
| SiamMask | ✔ | ✔ | ✔ | ✔ | pysot | ✔ | official | |
| SiamFC++ | ✔ | ✔ | ✔ | ✔ | pysot&got10k | ✔ | official | ✔ |
The implementation of simple face classification based on siamese network.
Add GOT10K toolkit and optimize the interface.
We use the VID data set for training .
The testing results are slightly lower than the paper(without hyperparameter adjustment).
Add GOT10K toolkit and optimize the interface.
We use YTB and VID data sets for training.
The testing results are slightly lower than the paper(without hyperparameter adjustment).
Add PYSOT toolkit and optimize the interface.
You can debug, train and test easily.
The results of testing are consistent with the paper.
Note that you shound have python3 environment.
Add PYSOT toolkit and optimize the interface.
The model is sensitive to learning rate.
Our results is higher than the original paper on VOT2018 dataset. EAO=0.403(Ours) EAO=0.393(Paper)
The paper mainly analyzed the impact of padding on the tracking network.
Support VScode single-step debugging.
Add test scripts for 4 drone datasets.
Change distributed multi-machine multi-GPU parallel to single-machine multi-GPU parallel.
Train SiamRPN++ AlexNet version using four datasets (training time is 3~4 days with 2 1080 GPUs ).
Support VScode single-step debugging.
Support testing and training.
The results of my test are inconsistent with the author's, please refer to my SiamMask branch.
Support VScode single-step debugging.
Add test scripts for 4 drone datasets.
Use GOT10K data set to retrain the AlexNet version, the training time is 15~20 hours (2 1080 GPUs).
- GPU NVIDIA 1080 8G
- CPU Intel® Xeon(R) CPU E5-2650 v4 @ 2.20GHz × 24
- CUDA 9.0
- Ubuntu 16.04
- PyTorch 1.1.0
- Python 3.7.3
Due to the limitation of computer configuration, i only choose some high speed algorithms for training and testing on several small tracking datasets
| Trackers | SiamFC | DaSiamRPN | DaSiamRPN | SiamRPN++ | SiamRPN | SiamFC++ | |
|---|---|---|---|---|---|---|---|
| Backbone | - | AlexNet | AlexNet(OTB/VOT) | AlexNet(BIG) | AlexNet(DW) | AlexNet(UP) | AlexNet |
| FPS | 85 | >120 | >120 | >120 | >120 | >120 | |
| OTB100 | AUC | 0.570 | 0.655 | 0.646 | 0.648 | 0.637 | 0.680 |
| DP | 0.767 | 0.880 | 0.859 | 0.853 | 0.851 | 0.884 | |
| UAV123 | AUC | 0.504 | 0.586 | 0.604 | 0.578 | 0.527 | 0.623 |
| DP | 0.702 | 0.796 | 0.801 | 0.769 | 0.748 | 0.781 | |
| UAV20L | AUC | 0.410 | 0.524 | 0.530 | 0.454 | 0.516 | |
| DP | 0.566 | 0.691 | 0.695 | 0.617 | 0.613 | ||
| DTB70 | AUC | 0.487 | 0.554 | 0.588 | 0.639 | ||
| DP | 0.735 | 0.766 | 0.797 | 0.826 | |||
| UAVDT | AUC | 0.451 | 0.593 | 0.566 | 0.632 | ||
| DP | 0.710 | 0.836 | 0.793 | 0.846 | |||
| VisDrone | AUC | 0.510 | 0.547 | 0.572 | 0.588 | ||
| DP | 0.698 | 0.722 | 0.764 | 0.784 | |||
| VOT2016 | A | 0.538 | 0.61 | 0.625 | 0.618 | 0.56 | 0.626 |
| R | 0.424 | 0.22 | 0.224 | 0.238 | 0.26 | 0.144 | |
| E | 0.262 | 0.411 | 0.439 | 0.393 | 0.344 | 0.460 | |
| Lost | 91 | 48 | 51 | 31 | |||
| VOT2018 | A | 0.501 | 0.56 | 0.586 | 0.576 | 0.49 | 0.577 |
| R | 0.534 | 0.34 | 0.276 | 0.290 | 0.46 | 0.183 | |
| E | 0.223 | 0.326 | 0.383 | 0.352 | 0.244 | 0.385 | |
| Lost | 114 | 59 | 62 | 39 |
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UAV123 BaiduYun password: 2iq4
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VOT2018 BaiduYun password: e5eh
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VisDrone2019 BaiduYun password: yxb6
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OTB2015 BaiduYun password: t5i1
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DTB70 BaiduYun password: e7qm
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ILSVRC2015 VID BaiDuYun password: uqzj
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ILSVRC2015 DET Link
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YTB-Crop511 BaiduYun password: ebq1 （used in siamrpn++ and siammask）
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COCO Link
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NFS BaiduYun password: vng1
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GOT10k BaiduYun password: uxds
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UAVDT BaiduYun password: keva
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YTB-VOS BaiduYun password: sf1m
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TColor128 BaiduYun password: 26d4
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DAVIS2017 BaiduYun password: c9qp
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YTB&VID BaiduYun password: 6vkz (used in siamrpn)
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TrackingNet BaiduYun password: nkb9 (Note that this link is provided by SiamFC++ author)
- OTB2013/2015 Github
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pysot-toolkit： OTB, VOT, UAV, NfS, LaSOT are supported.BaiduYun password: 2t2q
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got10k-toolkit：GOT-10k, OTB, VOT, UAV, TColor, DTB, NfS, LaSOT and TrackingNet are supported.BaiduYun password: vsar
BaiduYun password: fukj
You can scan the following QR code to join the SiamTrackers group. Let's communicate and make progress together!
[1] SiamFC
Bertinetto L, Valmadre J, Henriques J F, et al. Fully-convolutional siamese networks for object tracking.European conference on computer vision. Springer, Cham, 2016: 850-865.
[2] SiamRPN
Li B, Yan J, Wu W, et al. High performance visual tracking with siamese region proposal network.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 8971-8980.
[3] DaSiamRPN
Zhu Z, Wang Q, Li B, et al. Distractor-aware siamese networks for visual object tracking.Proceedings of the European Conference on Computer Vision (ECCV). 2018: 101-117.
[4] UpdateNet
Zhang L, Gonzalez-Garcia A, Weijer J, et al. Learning the Model Update for Siamese Trackers. Proceedings of the IEEE International Conference on Computer Vision. 2019: 4010-4019.
[5] SiamDW
Zhang Z, Peng H. Deeper and wider siamese networks for real-time visual tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 4591-4600.
[6] SiamRPN++
Li B, Wu W, Wang Q, et al. Siamrpn++: Evolution of siamese visual tracking with very deep networks.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 4282-4291.
[7] SiamMask
Wang Q, Zhang L, Bertinetto L, et al. Fast online object tracking and segmentation: A unifying approach. Proceedings of the IEEE conference on computer vision and pattern recognition. 2019: 1328-1338.
[8] SiamFC++
Xu Y, Wang Z, Li Z, et al. SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines. arXiv preprint arXiv:1911.06188, 2019.