Online Filtering Training Samples for Robust Visual Tracking (ACM MM2020)
- Baidu Yun 提取码：pitf
- Google Drive
| OTB2015 | Success | Precision |
|---|---|---|
| MDNet | 0.671 | 0.904 |
| MDNet+MetricNet | 0.681 | 0.910 |
| ECO | 0.666 | 0.903 |
| ECO+MetricNet | 0.678 | 0.926 |
| ATOM | 0.665 | 0.870 |
| ATOM+MetricNet | 0.675 | 0.881 |
| UAV123 | Success | Precision |
|---|---|---|
| MDNet | 0.540 | 0.754 |
| MDNet+MetricNet | 0.561 | 0.789 |
| ECO | 0.533 | 0.764 |
| ECO+MetricNet | 0.546 | 0.786 |
| ATOM | 0.621 | 0.832 |
| ATOM+MetricNet | 0.650 | 0.866 |
| LaSOT | Success | Norm Precision |
|---|---|---|
| MDNet | 0.390 | 0.430 |
| MDNet+MetricNet | 0.443 | 0.523 |
| ECO | 0.371 | 0.431 |
| ECO+MetricNet | 0.419 | 0.501 |
| ATOM | 0.503 | 0.574 |
| ATOM+MetricNet | 0.535 | 0.614 |
python 3.7
pytorch
ubuntu 16.04 + cuda-9.0
The pretrained models are also downloaded.
bash install.sh conda_install_path metricnetcd Train
python prepare_data.pypython train.pycd MDNet_MetricNet
python metric_tracking.pycd pytracking_MetricNet/pytracking
python run_tracker.py