FootVid F.C.

Football Video Frames Classification

The establishment of FootVid F.C. was guided by the simple idea:

Revolutionize the classification of match video frames using AI

In this repository you will find implementation of ML-based methods targeting the aforementioned task. Sample data available for the project are visualized below using the source package function: footvid.utils.images.plot_directories_representation.

Project Organisation

├── README.md          <- The top-level README for developers using this project.
│
├── data
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs
│   └── images         <- Folder for storing images used across the package documentation.
│
├── logs               <- Tensorboard model training logs.
│
├── models             <- Trained and serialized models, model predictions, or model summaries.
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering) and
│                         a short `-` delimited description, e.g. `00-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
|
├── poetry.lock        <- File to resolve and install all dependencies listed in the
│                         pyproject.toml file.
├── pyproject.toml     <- File orchestrating the project and its dependencies.
│
├── footvid            <- Source code for use in this project.

Notebooks

The project is designed to separate the particular modeling steps into notebooks. Notebook list:

• 00-images-integrity focuses on getting familiarity with data, examines images size, resolution and relation of positive to negative frames. It also generates a presentation of an example input.
• 01-train-valid-split is dedicated to dividing the data set into an appropriately represented training and validation set to avoid consequences of sampling bias like shown in the widely known The Literary Digest Presidential poll. Implementation of an algorithm that works in a quadratic worst-case time complexity and finds best splits in a semi-greedy manner guided by match_hash is also provided in this notebook.
• 10-resnet provides ResNet50 experiments setup. Three methodologies of fine-tuning were tested. First, optimizing only the weights of the last, fully-connected, layer. Second, additionally unfreezing 3rd and 4th ResNet layers. The last one, fine-tuning the whole neural net. Those operations are possible due to the recursive nature of the footvid.arena.freeze_layers function. The models were trained with the use of a safe SGD optimizer, avoiding the risk of unstable solutions obtained with adaptive optimizers.
• 11-model-selection shows method of selecting the best model with use of the TensorBoard training logs and prepares test dataset predictions.
• 12-gradcam renders Gradient-weighted Class Activation Mappings of the Champion model for 11 randomly chosen images from the validation set. It can be observed that the model is trying to extract a group of players located on the football pitch, which seems to be a fairly good heuristic for assessing whether the image is a match video frame or not. Whenever there is a camera close-up or something unrelated with the football match the model easily detects that it is not a match frame, which can be seen by the lack of "warm" areas on the heatmap. Examples below.

Grad-CAMs

True label: positive, prediction: 0.95

True label: negative, prediction: 0.13

Installation

If only the footvid source package functionalities are of interest then it is enough to run:

pip install git+https://github.com/mrtovsky/footvid.git

To interact with the notebooks e.g. rerun them, full project preparation is necessary. It can be done in the following few steps. First of all, you need to clone the repository:

git clone https://github.com/mrtovsky/footvid.git

Then, enter this directory and create a .env file that stores environment variable with the cloned repository path:

cd footvid/
touch .env
printf "REPOSITORY_PATH=\"$(pwd)\"" >> .env

Poetry

The recommended way of installing the full project is via Poetry package. If Poetry is not installed already, follow the installation instructions at the provided link. Then, assuming you have already entered the footvid directory, resolve and install dependencies using:

poetry install

Furthermore, you may want to attach a kernel with the already created virtual environment to Jupyter Notebook. This can be done by calling:

poetry run python -m ipykernel install --name=footvid-venv

This will make footvid-venv available in your Jupyter Notebook kernels.

pip

It is also possible to install the package in a traditional way, simply run:

pip install -e .

This will install the package in an editable mode. If you installed it inside of the virtual environment, then attaching it to the Jupyter Notebook kernel is the same as with the Poetry but the command is stripped from the first two elements (remember that the virtualenv needs to be activated beforehand):

python -m ipykernel install --name=footvid-venv

Results

Method	Accuracy score (train / validation)	Average precision (train / validation)
Fully-connected layer fine-tuning	0.9676 / 0.9780	0.9947 / 0.9949
Top2 CNN layers and FCL fine-tuning	0.9826 / 0.9824	0.9961 / 0.9970
Full network fine-tuning	0.9758 / 0.9802	0.9956 / 0.9950

More detailed training results can be displayed by opening the tensorboard:

tensorboard --logdir ./logs/ --host localhost

Judging by the prism of plain values, intuitively, best results brings the model that has fine-tuned layers responsible for learning more intricate, dataset-specific features and a classifier on top of that. Training the classification layer only may result in the worse results due to the fact that more complex features obtained from convolutional layers are not well tailored to the needs of a given problem. On the other hand, entire network fine-tuning, even the layers responsible for recognizing more general, universal shapes makes the model vulnerable to learning the noise present in smaller datasets. Training only the top layers of CNNs seems to find the perfect balance between the above-mentioned risks.

It should be remembered that this inference is based on bare results on a very small validation set, where there is a high risk of no statistical significance between the differences in the performance of models. If we want to actually check whether one estimator performs better than the other, we should use the appropriate test, e.g. combined 5x2 cv F-test. However, it is not within our interest in this project, so having to decide on one of the models mentioned, we will take the one that had the best results as our new champion and use it for preparing test predictions.

What's Next

Some ideas on how to continue the research work on this project:

• Training shallow classification model using features extracted with Histogram of Oriented Gradients (HOG) method.
• Using the already developed model to active learning - detect mislabeled frames.
• Clustering to group frames from the same match despite having different match_hash assigned to them.
• Redefining the classification problem into object detection and creating match frame detection heuristics guided by the presence of "football pitch landmarks".

Related Publications

Karpathy et al., Large-scale Video Classification with Convolutional Neural Networks

Key takeaways:

• CNNs' expected behavior is to learn increasingly complex patterns with successive, deeper layers - first layers will learn more generic features, on the contrary, deeper layers will be responsible for learning intricate, dataset-specific features. In the case of transfer learning, this will have a significant impact on the choice of "defrosted" layers to be fine-tuned.

• Most sport videos are biased towards displaying the objects of interest in the central region. Thus, images preprocessing proposed in this article includes:

  • cropping to center region,
  • resizing images to 200 × 200 pixels,
  • randomly sampling a 170 × 170 region,
  • randomly flipping the images horizontally with 50% probability.

  The final absolute size of the images is too small for our problem because the pre-trained torchvision.models require the image height and width of at least 224 x 224 pixels but the idea is definitely worth attention, after appropriate adjustment to the problem under consideration.

Simeon Jackman, Football Shot Detection using Convolutional Neural Networks

Key takeaways:

• Confirmation of correctness of the appliaction of transfer learning in the context of football match TV broadcast analysis, with the use of pre-trained models trained on the ImageNet dataset.