NanoTrack

• 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, it can run at >120FPS on Apple M1 CPU.

• Experiments show that NanoTrack algorithm has good performance on tracking datasets. | Trackers | Backbone | ModeSize | VOT2018 EAO | VOT2019 EAO | GOT-10k-Val AO | GOT-10k-Val SR | DTB70 Success | DTB70 Precision | | :-------------------------- | :----------: | :------: | :---------: | :---------: | :------------: | :------------: | :-----------: | :-------------: | | NanoTrack | MobileNetV3 | 2.2MB | 0.311 | 0.247 | 0.604 | 0.724 | 0.532 | 0.727 | | 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 |

• 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. NanoTrack only uses GOT-10k dataset to train, which only takes two hours on GPU3090.

Mac

PC demo

• 1. Modify your own CMakeList.txt
• 2. Build (Apple M1 CPU)

  $ sh make_macos_arm64.sh 
  

Android

Android demo

• 1. Modify your own CMakeList.txt
• 2. Download(password: 6cdd) OpenCV and NCNN libraries for Android

Reference

https://github.com/Tencent/ncnn

https://github.com/Z-Xiong/LightTrack-ncnn

https://github.com/FeiGeChuanShu/ncnn_Android_LightTrack