# SiamTrackers **Repository Path**: jankin987/SiamTrackers ## Basic Information - **Project Name**: SiamTrackers - **Description**: 储宏琳的siamTrackers - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 1 - **Created**: 2021-01-25 - **Last Updated**: 2025-07-21 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # SiamTrackers # Description - [bilibili](https://www.bilibili.com/video/BV1pt4y1e7PY/?spm_id_from=333.788.recommend_more_video.8) - [Tutorial](./Tutorial.md) - [SiamFace](./1-SiamFace) ![image](./image/siamese.png) ``` The implementation of simple face classification based on siamese network. ``` - [2016-ECCV-SiamFC](./SiamFC) ![image](./image/siamfc.png) ``` 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). ``` - [2018-CVPR-SiamRPN](./SiamRPN) ![image](./image/siamrpn.png) ``` 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). ``` - [2018-ECCV-DaSiamRPN](./DaSiamRPN) ``` 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. ``` - [2019-ICCV-UpdateNet](./UpdateNet) ![image](./image/updatenet.png) ``` 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) ``` - [2019-CVPR-SiamDW](./SiamDW) ``` The paper mainly analyzed the impact of padding on the tracking network. ``` - [2019-CVPR-SiamRPNpp](./SiamRPNpp) ![image](./image/siamrpn++.png) ``` 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 SiamRPNpp AlexNet version using four datasets (training time is 3~4 days with 2 1080 GPUs ). ``` - [2019-CVPR-SiamMask](./SiamMask) ![image](./image/siammask2.png) ``` Support VScode single-step debugging. Support testing and training. ``` - [2020-AAAI-SiamFCpp](./SiamFCpp) ![image](./image/siamfc++.png) ``` 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). ``` - [2020-CVPR-SiamCAR](./SiamCAR) ![image](./image/siamcar.png) ``` Support VScode single-step debugging. ``` - [2020-CVPR-SiamBAN](./SiamBAN) ![image](./image/siamban.png) ``` Support VScode single-step debugging. Support testing and training.◊ ``` - [2021-TrTr](./TrTr) ![image](./image/trtr.png) ``` Support VScode single-step debugging. Support testing and training. ``` - [NanoTrack](./NanoTrack) ![image](./image/nanotrack_network.png) # Experiment - [x] CUDA 10.0 - [x] Ubuntu 18 - [x] PyTorch 1.7.0 - [x] Python 3.8 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 | SiamRPN | SiamRPN | DaSiamRPN |DaSiamRPN | SiamRPNpp | SiamRPNpp | SiamRPNpp | SiamRPNpp | SiamFCpp |SiamFCpp | |:------------:|:-----:|:--------: | :------: |:------: |:------: |:------:|:------:|:------:|:------:|:------:|:------:|:------:| | Train Set | | GOT | official | GOT | official | official | official | GOT | GOT | GOT | GOT | official | | Backbone | | AlexNet | AlexNet | AlexNet | AlexNet | AlexNet-DA | AlexNet-DW | AlexNet-DW | AlexNet-UP | AlexNet-DA | AlexNet |AlexNet| | FPS | | 85 | >120 | >120 | >120 | >120 | >120 | >120 | >120 | >120 | >120 | >120 | | | | | | | | | | | | | | | | OTB100 | AUC | 0.589 | 0.637 | 0.603 | 0.655 | 0.646 | 0.648 | 0.623 | 0.619 | 0.634 | 0.629 | **0.680** | | | DP | 0.794 | 0.851 | 0.820 | 0.880 | 0.859 | 0.853 | 0.837 | 0.823 | 0.846 | 0.830 | **0.884** | | | | | | | | | | | | | | | | UAV123 | AUC | 0.504 | 0.527 | | 0.586 | 0.604 | 0.578 | | | | | **0.623** | | | DP | 0.702 | 0.748 | | 0.796 | **0.801** | 0.769 | | | | | 0.781 | | | | | | | | | | | | | | | | UAV20L | AUC | 0.410 | 0.454 | | | 0.524 | **0.530** | | | | | 0.516 | | | DP | 0.566 | 0.617 | | | **0.691** | 0.695 | | | | | 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-Train | AUC | 0.510| | | | 0.547 | 0.572 | | | | | **0.588** | | | DP | 0.698| | | | 0.722 | 0.764 | | | | | **0.784** | | | | | | | | | | | | | | | | VOT2016 | A | 0.538 | 0.56 | | 0.61 | 0.625 | 0.618 | 0.582 | 0.592 | | 0.612 | **0.626** | | | R | 0.424 | 0.26 | | 0.22 | 0.224 | 0.238 | 0.266 | 0.252 | | 0.266 | **0.144** | | | E | 0.262 | 0.344 | | 0.411 | 0.439 | 0.393 | 0.372 | 0.365 | | 0.357 | **0.460** | | |Lost | 91 | | | | 48 | 51 | 57 | 54 | | 57 | 31 | | | | | | | | | | | | | | | | VOT2018 | A | 0.501 | 0.49 | | 0.56 | **0.586** | 0.576 | 0.563 | 0.555 | 0.557 | 0.584 | 0.577 | | | R | 0.534 | 0.46 | | 0.34 | 0.276 | 0.290 | 0.375 | 0.384 | 0.412 | 0.342 | **0.183** | | | E | 0.223 | 0.244 | | 0.326 | 0.383 | 0.352 | 0.300 | 0.292 | 0.275 | 0.308 | **0.385** | | | Lost | 114 | | | | 59 | 62 | 80 | 82 | 88 | 73 | 39 | # Dataset - **All json files** [BaiduYun](https://pan.baidu.com/s/1RL1kwdP93fdBVOrPc5y0bQ) parrword: xm5w (The json files are provided by [pysot](https://github.com/STVIR/pysot)) - **OTB2015** [BaiduYun](https://pan.baidu.com/s/1ZjKgRMYSHfR_w3Z7iQEkYA) password: t5i1 - **VOT2016** [BaiduYun](https://pan.baidu.com/s/1ihsivizX62WhsKBFxwu84w) password: v7vq - **VOT2018** [BaiduYun](https://pan.baidu.com/s/1MOWZ5lcxfF0wsgSuj5g4Yw) password: e5eh - **VOT2019** [BaiduYun](https://pan.baidu.com/s/1HqugngSFKfGl8NGXiRlR_Q) password: p4fi - **VOT2020** [BaiduYun](https://pan.baidu.com/s/14KqEVJA10ykO4w4L5gtTjA) password: x93i - **UAV123** [BaiduYun](https://pan.baidu.com/s/1AhNnfjF4fZe14sUFefU3iA) password: 2iq4 - **DTB70** [BaiduYun](https://pan.baidu.com/s/1kfHrArw0aVhGPSM91WHomw) password: e7qm - **UAVDT** [BaiduYun](https://pan.baidu.com/s/1K8oo53mPYCxUFVMXIGLhVA) password: keva - **VisDrone2019** [BaiduYun](https://pan.baidu.com/s/1Y6ubKHuYX65mK_iDVSfKPQ) password: yxb6 - **TColor128** [BaiduYun](https://pan.baidu.com/s/1v4J6zWqZwj8fHi5eo5EJvQ) password: 26d4 - **NFS** [BaiduYun](https://pan.baidu.com/s/1ei54oKNA05iBkoUwXPOB7g) password: vng1 - **GOT10k** [BaiduYun](https://pan.baidu.com/s/172oiQPA_Ky2iujcW5Irlow) password: uxds (SiamFC-GOT, SiamRPN-GOT, SiamDW, SiamFCpp) - **LaSOT** [BaiduYun](https://pan.baidu.com/s/1A_QWSzNdr4G9CR6rZ7n9Mg) password: ygtx (SiamDW, SiamFCpp) - **YTB&VID** [BaiduYun](https://pan.baidu.com/s/1gF8PSZDzw-7EAVrdYHQwsA) password: 6vkz (SiamRPN) - **ILSVRC2015 VID** [BaiDuYun](https://pan.baidu.com/s/1CXWgpAG4CYpk-WnaUY5mAQ) password: uqzj (SiamFC, SiamRPNpp, SiamMask, siamdw, SiamFCpp) - **ILSVRC2015 DET** [BaiDuYun](https://pan.baidu.com/s/1t2IgiYGRu-sdfOYwfeemaQ) password: 6fu7 (SiamRPNpp, SiamMask, SiamDW, SiamFCpp) - **YTB-Crop511** [BaiduYun](https://pan.baidu.com/s/112zLS_02-Z2ouKGbnPlTjw) password: ebq1 (SiamRPNpp, SiamMask, SiamDW,SiamFCpp) - **COCO** [BaiduYun](https://pan.baidu.com/s/17AMGS2ezLVd8wFI2NbJQ3w) password: ggya (SiamRPNpp, SiamMask, SiamDW, SiamFCpp) - **YTB-VOS** [BaiduYun](https://pan.baidu.com/s/1WMB0q9GJson75QBFVfeH5A) password: sf1m (SiamMask) - **DAVIS2017** [BaiduYun](https://pan.baidu.com/s/1JTsumpnkWotEJQE7KQmh6A) password: c9qp (SiamMask) - **TrackingNet** [BaiduYun](https://pan.baidu.com/s/1PXSRAqcw-KMfBIJYUtI4Aw) password: nkb9 (Note that this link is provided by SiamFCpp author) (SiamFCpp) # Toolkit ### Matlab version - **OTB2013/2015** [Github](https://github.com/HonglinChu/visual_tracker_benchmark) - **UAVDT** [BaiduYun](https://pan.baidu.com/s/1NdpaWZxv5hGfKnIqJznWYA) password: ehit - **VOT2016-toolkit** [BaiduYun](https://pan.baidu.com/s/1RbmH-fVExBpHv3TgjHzYGg) password: 272e - **VOT2018-toolkit** [BaiduYun](https://pan.baidu.com/s/1crv4XSFK6zQp2LiZtJcrPw) password: xpkb ### Python version - **pysot-toolkit**： OTB, VOT, UAV, NfS, LaSOT are supported.[BaiduYun](https://pan.baidu.com/s/1H2Hc4VXsWahgNjDZJP8jaA) password: 2t2q - **got10k-toolkit**：GOT-10k, OTB, VOT, UAV, TColor, DTB, NfS, LaSOT and TrackingNet are supported.[BaiduYun](https://pan.baidu.com/s/1OS80_OPtZoo0ZFKzfCOFzg) password: vsar # Papers [BaiduYun](https://pan.baidu.com/s/1nyXMesdAUHzdSQkM88AvWQ) password: fukj # Reference ``` [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] SiamRPNpp Li B, Wu W, Wang Q, et al. SiamRPNpp: 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] SiamFCpp Xu Y, Wang Z, Li Z, et al. SiamFCpp: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines. AAAI, 2020. [9] SiamCAR Guo D , Wang J , Cui Y , et al. SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2020. [10] SiamBAN Chen Z, Zhong B, Li G, et al. Siamese box adaptive network for visual tracking[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 6668-6677. [11] TrTr Zhao M, Okada K, Inaba M. TrTr: Visual Tracking with Transformer[J]. arXiv preprint arXiv:2105.03817, 2021. ```