SiamTrackers

The code will come soon！ https://www.bilibili.com/video/BV1Y64y1T7qs/

Trackers	Debug	Train	Test	Evaluation	Comment	Toolkit	GPU	Version
Siamese	√	√	√	√	√		√	-
SiamFC	√	√	√	√	√	got10k	√	unofficial
SiamRPN	√	√	√	√	√	got10k	√	unofficial
DaSiamRPN	√		√	√	√	pysot	√	official
UpdateNet	√	√	√	√	√	pysot	√	unofficial
SiamDW	√	√	√	√	√	-	√	official
SiamRPN++	√	√	√	√	√	pysot	√	official
SiamMask	√	√	√	√	√	pysot	√	official
SiamFC++	√	√	√	√	√	pysot&got10k	√	official

DESCRIPTION

Siamese

基于孪生网络的简单人脸分类实现，支持训练和测试,

2016-ECCV-SiamFC

添加got10k评估工具;对接口进行优化;可评估,可训练和测试;使用VID数据集进行训练,训练速度还是比较快的,记不太清了,大概几个hours;复现结果略低于论文(没有进行超参调节); 使用GOT10K数据集进行训练效果要比论文的结果好一些

2018-CVPR-SiamRPN

添加got10k评估工具;可评估,可训练和测试;使用YTB和VID数据集进行训练，训练时长>24 hours,复现结果略低于论文(没有进行超参调节);

2018-ECCV-DaSiamRPN

支持VScode单步调试,加pysot评估工具;支持一键测试和评估;测试结果和论文一致;不支持训练;

2019-ICCV-UpdateNet

复现updatenet网络;可测试,训练,评估模型;目前复现发现模型对学习率比较敏感,还在摸索中;训练时间<1 hour,测试时间每个epoch~10min

2019-CVPR-SiamDW 之前没有关注这个算法,最近看到这个算法速度还是很快的,后面有时间再复现一下
2019-CVPR-SiamRPN++

支持VScode单步调试；对训练和测试的接口进行了优化；对代码进行部分注释; 修改训练模式,将分布式多机多GPU并行;改成单机多GPU并行;使用四个数据集重新训练SiamRPN++(alexnet版本,训练时间3~4days);在没有进行调超参的情况下精度和论文比较接近

2019-CVPR-SiamMask
支持VScode单步调试;对训练和测试的接口进行了优化;对代码进行部分注释;
2020-AAAI-SiamFC++

支持VScode单步调试,对训练和测试的接口进行了优化;对代码进行部分注释;使用GOT10K数据集重新训练alexnet版本,训练时长~20 hours,测试精度和论文比较接近(备注：官方代码封装的非常好,用到了很多的编程技巧,真的非常考验一个人的代码功底, 但是也给学者理解代码带来很大挑战:(,另外敬请期待我后续更新精简的版本吧:) )

MODEL

SiamRPNVOT.model link: https://pan.baidu.com/s/1V7GMgurufuILhzTSJ4LsYA password: p4ig
SiamRPNOTB.model link: https://pan.baidu.com/s/1mpXaIDcf0HXf3vMccaSriw password: 5xm9
SiamRPNBIG.model link: https://pan.baidu.com/s/10v3d3G7BYSRBanIgaL73_Q password: b3b6

EXPERIMENTAL

My environment

GPU Nvidia-1080 8G
CPU Intel® Xeon(R) CPU E5-2650 v4 @ 2.20GHz × 24
CUDA 9.0
System ubuntu 16.04 64 bits
pytorch 1.1.0
python 3.7.3

Note: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	fps>120	120	180	140	160	180	140

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

DATASETS

UAV123 link: https://pan.baidu.com/s/1AhNnfjF4fZe14sUFefU3iA password: 2iq4
VOT2018 link: https://pan.baidu.com/s/1MOWZ5lcxfF0wsgSuj5g4Yw password: e5eh
VisDrone2019 link: https://pan.baidu.com/s/1Y6ubKHuYX65mK_iDVSfKPQ password: yxb6
OTB2015 link: https://pan.baidu.com/s/1ZjKgRMYSHfR_w3Z7iQEkYA password: t5i1
DTB70 link: https://pan.baidu.com/s/1kfHrArw0aVhGPSM91WHomw password: e7qm
ILSVRC2015 VID link: https://pan.baidu.com/s/1CXWgpAG4CYpk-WnaUY5mAQ password: uqzj
NFS link: https://pan.baidu.com/s/1ei54oKNA05iBkoUwXPOB7g password: vng1
GOT10k link: https://pan.baidu.com/s/172oiQPA_Ky2iujcW5Irlow password: uxds
UAVDT link: https://pan.baidu.com/s/1K8oo53mPYCxUFVMXIGLhVA password: keva
YTB-VOS link: https://pan.baidu.com/s/1WMB0q9GJson75QBFVfeH5A password: sf1m
YTB-Crop511 （used in siamrpn++ and siammask）link: https://pan.baidu.com/s/112zLS_02-Z2ouKGbnPlTjw password: ebq1
TColor128 link: https://pan.baidu.com/s/1v4J6zWqZwj8fHi5eo5EJvQ password: 26d4
DAVIS2017 link: https://pan.baidu.com/s/1JTsumpnkWotEJQE7KQmh6A password: c9qp
YTB&VID (used in siamrpn) link: https://pan.baidu.com/s/1gF8PSZDzw-7EAVrdYHQwsA password: 6vkz
TrackingNet link https://pan.baidu.com/s/1PXSRAqcw-KMfBIJYUtI4Aw code: nkb9 (Note that this link is provided by SiamFC++ author)

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] 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++