zj5559/MetricNet

Online Filtering Training Samples for Robust Visual Tracking (ACM MM2020)

Python

MetricNet

Online Filtering Training Samples for Robust Visual Tracking (ACM MM2020)

Paper link

Baidu Yun 提取码：pitf
Google Drive

Results

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

Requirments

python 3.7
pytorch
ubuntu 16.04 + cuda-9.0

Installation

The pretrained models are also downloaded.

bash install.sh conda_install_path metricnet

Train

Prepare dataset (LaSOT)

cd Train
python prepare_data.py

Train MetricNet

python train.py

Eval

Integrate MetricNet into MDNet

cd MDNet_MetricNet
python metric_tracking.py

Integrate MetricNet into ECO/ATOM

cd pytracking_MetricNet/pytracking
python run_tracker.py