In this project, I detected the players and classified them using background subtraction methods and artificial neural networks. The primary libraries that I used are openCV and PyTorch.
In part1.py, the only functionality is reading the video from local storage and detect players. In part2_classify.py, the classification is added on top of part1.py. Some scripts were developed to generate the dataset and train the neural net.
The input videos are from Soccer video and player position dataset. I used about 4 minutes of the first half to train my neural network. And a video from the second half was used to evaluate the model. Accuracy scores were about 97%. The codes are optimized to run on GPU.
Here is an image of top view of the field with a rendered map of players position:
I automatically generated the patches of players and labeled them by checking the color by openCV. Here is a figure containing 40 patches (from test dataset) and label/prediction as the title:
