Learning Spatio-Temporal Transformer for Visual Tracking
STARK
The official implementation of the paper Learning Spatio-Temporal Transformer for Visual Tracking
Hiring research interns for visual transformer projects: [email protected]
Highlights
End-to-End, Post-processing Free
STARK is an end-to-end tracking approach, which directly predicts one accurate bounding box as the tracking result.
Besides, STARK does not use any hyperparameters-sensitive post-processing, leading to stable performances.
Real-Time Speed
STARK-ST50 and STARK-ST101 run at 40FPS and 30FPS respectively on a Tesla V100 GPU.
Strong performance
| Tracker | LaSOT (AUC) | GOT-10K (AO) | TrackingNet (AUC) |
|---|---|---|---|
| STARK | 67.1 | 68.8 | 82.0 |
| TransT | 64.9 | 67.1 | 81.4 |
| TrDiMP | 63.7 | 67.1 | 78.4 |
| Siam R-CNN | 64.8 | 64.9 | 81.2 |
Purely PyTorch-based Code
STARK is implemented purely based on the PyTorch.
Install the environment
Option1: Use the Anaconda
conda create -n stark python=3.6
conda