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SCCvSD

Sports Camera Calibration via Synthetic Data

The original implemenation uses Matlab. This is a re-implementation.

The two-GAN code: https://github.com/lood339/pytorch-two-GAN
Link: https://arxiv.org/abs/1810.10658

Install required package via conda:
conda install -c anaconda numpy
conda install -c anaconda scipy
conda install -c conda-forge pyflann
conda install -c conda-forge opencv

If no GPU:
conda install pytorch-cpu torchvision-cpu -c pytorch

Pre-processing:

  1. Generate HoG feature (optional)
    cd python/hog python generate_test_feature_hog.py
    python generate_database_hog.py

Put two generated .mat files to ./data/features

  1. train a network to generate deep feature (optional)
    Here, we use 10K cameras for an example.
    cd python/deep
    python generate_train_data.py
    Put the generated .mat file to ./data
    bash network_train.sh
    It generates a 'network.pth' file.
    bash network_test.sh
    It generates a .mat file which has 'features' and 'cameras'.

A demo script in testing phase:
python/demo.py
python/demo_uot.py # contributed by jiangwei221
Example 1: use deep feature
python demo.py --feature-type 'deep' --query-index 0 It uses pre-trained-deep-features.

Example 2: use HoG feature
python demo.py --feature-type 'HoG' --query-index 0

Example 3: run all testing example of UoT dataset
python demo_uot.py --feature-type 'deep'

You wil get the result:
mean IoU for refined homogrpahy 0.948
median IoU for refined homogrpahy 0.964
Slightly better than the result in the paper.

To do:

  1. Refine train siamese network and extract deep feature.
  2. Accuracy of HoG feature is lower than the matlab implementation (using vlfeat)