SiamTrackers

October 26, 2023 · View on GitHub

NanoTrackV1

Experiment

NanoTrack is a lightweight and high speed tracking network which mainly referring to SiamBAN and LightTrack. It is suitable for deployment on embedded or mobile devices. In fact, V1 and V2 can run at > 200FPS on Apple M1 CPU.

Trackers	Backbone Size(*.onnx)	Head Size (*.onnx)	FLOPs	Parameters
NanoTrackV1	752K	384K	75.6M	287.9K
NanoTrackV2	1.0M	712K	84.6M	334.1K
NanoTrackV3	1.4M	1.1M	115.6M	541.4K

Experiments show that NanoTrack has good performance on tracking datasets.

Trackers	Backbone	Model Size(*.pth)	VOT2018 EAO	VOT2019 EAO	GOT-10k-Val AO	GOT-10k-Val SR	DTB70 Success	DTB70 Precision
NanoTrackV1	MobileNetV3	2.4MB	0.311	0.247	0.604	0.724	0.532	0.727
NanoTrackV2	MobileNetV3	2.0MB	0.352	0.270	0.680	0.817	0.584	0.753
NanoTrackV3	MobileNetV3	3.4MB	0.449	0.296	0.719	0.848	0.628	0.815
CVPR2021 LightTrack	MobileNetV3	7.7MB	0.418	0.328	0.75	0.877	0.591	0.766
WACV2022 SiamTPN	ShuffleNetV2	62.2MB	0.191	0.209	0.728	0.865	0.572	0.728
ICRA2021 SiamAPN	AlexNet	118.7MB	0.248	0.235	0.622	0.708	0.585	0.786
IROS2021 SiamAPN++	AlexNet	187MB	0.268	0.234	0.635	0.73	0.594	0.791

For NanoTrackV1, we provide Android demo and MacOS demo based on ncnn inference framework.
We also provide PyTorch code. It is friendly for training with much lower GPU memory cost than other models. NanoTrackV1 only uses GOT-10k dataset to train, which only takes two hours on RTX3090.

OpenCV API

https://docs.opencv.org/4.x/d8/d69/classcv_1_1TrackerNano.html

Dataset

All json files BaiduYun parrword: xm5w (The json files are provided by pysot)

Test

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

Train

GOT10k BaiduYun password: uxds
LaSOT BaiduYun password: ygtx
ILSVRC2015 VID BaiDuYun password: uqzj
ILSVRC2015 DET BaiDuYun password: 6fu7
YTB-Crop511 BaiduYun password: ebq1
COCO BaiduYun password: ggya
TrackingNet BaiduYun password: nkb9 (Note that this link is provided by SiamFCpp author)

Mask

YTB-VOS BaiduYun password: sf1m
DAVIS2017 BaiduYun password: c9qp

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. 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.

[12] LightTrack 
Yan B, Peng H, Wu K, et al. Lighttrack: Finding lightweight neural networks for object tracking via one-shot architecture search[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 15180-15189.