Automatisk igenkänning av fotbollsmål med deep learning : En jämförelse av 2D och 3D Residual Networks
Year: 2024
Type: dissertation
Abstract: Advancements in deep-learning architectures have revolutionized computer vision tasks across various domains, including sports. One such task is the creation of highlight clips, which involves editing broadcast sports events to feature the most important or interesting moments. This thesis focuses on automating parts of the highlight clip creation process for football matches, a currently manual and labor-intensive task for Sveriges Television AB (SVT), the Swedish national public service broadcast company. Building on prior work in football action recognition using Convolutional Neural Networks (CNNs), this study explores the integration of temporal information through 3D convolutions. More specifically, it compares the performance of a traditional 2D ResNet-50 model with a 3D ResNet-50 model, which processes sequences of frames using inflated convolution and pooling kernels as opposed to the standard 2D operations. The experiments focused on classifying football goals, as they are among the most significant highlights of a game and present a substantial classification challenge. The results show that while the 2D ResNet-50 achieves better classification performance than its 3D counterpart at higher sample rates, its performance degrades as the sample rate decreases. The 2D model retains accuracy, precision, and recall better than the 3D model on more challenging data, where the differences between goal and non-goal examples are more subtle. However, the 3D model has a potential advantage of shorter inference time on longer video sequences. In conclusion, the 2D ResNet-50 performs surprisingly well in classifying goals without learning any temporal context, demonstrating its potential effectiveness for automatic highlight detection in a production environment. This finding suggests that a simpler 2D model can efficiently be used to automate parts of highlight clip creation, potentially reducing costs from manual labor for broadcasters like SVT.
Author: Karimi Valdani, Adrian
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7036443676