mustansarfiaz / SiamTrackers

(2020)The PyTorch version of Siamese ，SiamFC，SiamRPN，DaSiamRPN，UpdateNet，SiamDW，SiamRPN++, SiamMask，and SiamFC++ ; Visual object tracking based on deep learning

SiamTrackers

Description

https://www.bilibili.com/video/BV1Y64y1T7qs/

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	✔

Some of the codes that have not been uploaded have official links in the corresponding folders.

Siamese

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-SiamRPN++

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 Retrain SiamRPN++ 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-SiamFC++

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) The results are close to the paper

Model

• SiamRPNVOT.model BaiDuYun password: p4ig

• SiamRPNOTB.model BaiDuYun password: 5xm9

• SiamRPNBIG.model BaiDuYun password: b3b6

Experiment

• 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		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

Dataset

• UAV123 BaiDuYun password: 2iq4

• VOT2018 BaiDuYun password: e5eh

• VisDrone2019 BaiDuYun password: yxb6

• OTB2015 BaiDuYun password: t5i1

• DTB70 BaiDuYun password: e7qm

• ILSVRC2015 VID BaiDuYun password: uqzj

• NFS BaiDuYun password: vng1

• GOT10k BaiDuYun password: uxds

• UAVDT BaiDuYun password: keva

• YTB-VOS BaiDuYun password: sf1m

• YTB-Crop511 BaiDuYun password: ebq1 （used in siamrpn++ and siammask）

• TColor128 BaiDuYun password: 26d4

• DAVIS2017 BaiDuYun password: c9qp

• YTB&VID BaiDuYun password: 6vkz (used in siamrpn)

• TrackingNet BaiDuYun password: nkb9 (Note that this link is provided by SiamFC++ author)

Toolkit

OTB-Toolkit

This is an optimized OTB toolkit (matlab version).
Support multi-thread parameter search, multi-thread running dataset.
Visualize the position error curve and overlap rate curve for each frame.

🌟Welcome To Join Us! 🌟

You can scan the following QR code to join the SiamTrackers group. Let's communicate and make progress together!

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++

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.