SiamTrackers

Description

Year	Conf	Trackers	Debug	Train	Test			Data	Set			Toolkit	Source
						VID	DET	COCO	YTB	GOT	LaSOT
2016	ECCV	SiamFC	✔	✔	✔	✔						got10k	unofficial
		SiamFC	✔	✔	✔					✔		got10k	unofficial
2018	CVPR	SiamRPN	✔	✔	✔	✔			✔			got10k	unofficial
		SiamRPN	✔	✔	✔					✔		got10k	unofficial
2018	ECCV	DaSiamRPN	✔		✔	✔	✔	✔	✔			pysot	official
		DaSiamRPN	✔	✔	✔					✔		pysot	unofficial
2019	ICCV	UpdateNet(FC)	✔	✔	✔						✔	pysot	unofficial
		UpdateNet(UP)	✔	✔	✔						✔	pysot	unofficial
		UpdateNet(DA)	✔	✔	✔						✔	pysot	official
		UpdateNet(DW)	✔	✔	✔						✔	pysot	unofficial
2019	CVPR	SiamDW(FC)	✔	✔	✔					✔		got10k	unofficial
		SiamDW(UP)	✔	✔	✔					✔		got10k	unofficial
2019	CVPR	SiamRPNpp(DW)	✔	✔	✔	✔	✔	✔	✔			pysot	official
		SiamRPNpp(DW)	✔	✔	✔					✔		pysot	unofficial
		SiamRPNpp(UP)	✔	✔	✔					✔		pysot	unofficial
		SiamRPNpp(DA)	✔	✔	✔					✔		pysot	unofficial
		SiamRPNpp(ResNet)	✔	✔	✔	✔	✔	✔	✔			pysot	official
2019	CVPR	SiamMask	✔	✔	✔	✔	✔	✔	✔			pysot	official
2020	AAAI	SiamFCpp	✔	✔	✔	✔	✔	✔	✔	✔	✔	pysot&got10k	official
		SiamFCpp	✔	✔	✔					✔		pysot&got10k	unofficial
		SiamFCpp(GoogleNet)	✔	✔	✔	✔	✔	✔	✔	✔	✔	pysot&got10k	official

• 哔哩哔哩
• 使用教程
• SiamFace

The implementation of simple face classification based on siamese network.

• 2016-ECCV-SiamFC

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

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

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
  

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

The paper mainly analyzed the impact of padding on the tracking network. 

• 2019-CVPR-SiamRPNpp

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

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.

• 2020-AAAI-SiamFCpp

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).

Experiment

• GPU NVIDIA 1080 8G x 2
• 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	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 parrword: xm5w (The json files are provided by pysot)

• OTB2015 BaiduYun password: t5i1

• VOT2016 BaiduYun password: v7vq

• VOT2018 BaiduYun password: e5eh

• VOT2019 BaiduYun password: p4fi

• VOT2020 BaiduYun password: x93i

• UAV123 BaiduYun password: 2iq4

• DTB70 BaiduYun password: e7qm

• UAVDT BaiduYun password: keva

• VisDrone2019 BaiduYun password: yxb6

• TColor128 BaiduYun password: 26d4

• NFS BaiduYun password: vng1

• GOT10k BaiduYun password: uxds (SiamFC-GOT, SiamRPN-GOT, SiamDW, SiamFCpp)

• LaSOT BaiduYun password: ygtx (SiamDW, SiamFCpp)

• YTB&VID BaiduYun password: 6vkz (SiamRPN)

• ILSVRC2015 VID BaiDuYun password: uqzj (SiamFC, SiamRPNpp, SiamMask, siamdw, SiamFCpp)

• ILSVRC2015 DET BaiDuYun password: 6fu7 (SiamRPNpp, SiamMask, SiamDW, SiamFCpp)

• YTB-Crop511 BaiduYun password: ebq1 (SiamRPNpp, SiamMask, SiamDW,SiamFCpp)

• COCO BaiduYun password: ggya (SiamRPNpp, SiamMask, SiamDW, SiamFCpp)

• YTB-VOS BaiduYun password: sf1m (SiamMask)

• DAVIS2017 BaiduYun password: c9qp (SiamMask)

• TrackingNet BaiduYun password: nkb9 (Note that this link is provided by SiamFCpp author) (SiamFCpp)

Toolkit

Matlab version

• OTB2013/2015 Github

• UAVDT BaiduYun password: ehit

• VOT2016-toolkit BaiduYun password: 272e

• VOT2018-toolkit BaiduYun password: xpkb

Python version

• pysot-toolkit： OTB, VOT, UAV, NfS, LaSOT are supported.BaiduYun password: 2t2q

• got10k-toolkit：GOT-10k, OTB, VOT, UAV, TColor, DTB, NfS, LaSOT and TrackingNet are supported.BaiduYun password: vsar

Papers

BaiduYun 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. arXiv preprint arXiv:1911.06188, 2019.