- Python 3.6
- GPU Memory >= 6G
- Numpy
- Pytorch 0.3+
- timm
pip install timmfor Swin-Transformer with Pytorch >1.7.0 - pretrainedmodels via
pip install pretrainedmodels - [Optional] apex (for float16)
- [Optional] pretrainedmodels
- Install Pytorch from http://pytorch.org/
- Install Torchvision from the source
Download Market1501 Dataset [Google] [Baidu]
Preparation: Put the images with the same id in one folder. You may use
python prepare.py
Remember to change the dataset path to your own path.
Adapting the method from Person_reID_baseline_pytorch. We train a model by
python train.py --gpu_ids 0 --name ft_ResNet50 --train_all --batchsize 32 --data_dir your_data_path
--gpu_ids which gpu to run.
--name the name of model.
--data_dir the path of the training data.
--train_all using all images to train.
--batchsize batch size.
--erasing_p random erasing probability.
Train a model with random erasing by
python train.py --gpu_ids 0 --name ft_ResNet50 --train_all --batchsize 32 --data_dir your_data_path --erasing_p 0.5
In test.py, extract_feature() helps to generate the feature representations, and spatial_temporal_distribution() function is used to generate the spatial and temporal correlation.
The code to solve the MFG will not be published for now.
To track query objects, run
python mfg.py --reid_method nn --split 32 --spatial 0.1 --temporal 0.1
--split is the stepsize for your MFG.
--spatial is the threshold for spatial correlation filtering.
--temporal is the threshold for temporal correlation filtering.
Extensive experiments on a six-camera dataset demonstrate that, with the help of our dynamic correlation models, the overall workload can be reduced by 36% in general. For queries with a large search space, the tracking cost can be reduced by 40$\times$ with 6% precision improvement.
All results drawing functions are in utils.py.