Abstracts

Penalty Kick Trajectory Prediction in Soccer Videos Using Digital Image Processing and a Deep Neural Network Model

by Chifu Vin-Nnajiofor

The ever-changing and rapid advances in computer vision, machine learning has paved way to unparalleled likelihoods in the broad world of sports, most especially in soccer. In current times, many professional sports teams use artificial intelligence and machine learning to improve the training and develop better strategies for competition. Great emphasis has been placed on collecting data, implementing models and techniques that help simplify set tasks by the teams. In this thesis, we developed an effective way of applying image and video processing techniques, deep learning, and machine learning models to predict penalty kicks in a soccer game. Specifically, the deep neural networks are used to analyze soccer videos and to predict penalty kicks’ trajectories. The image and video enhancement techniques are used to adjust the pixel values in the videos and improve the performance of the model which reduces the average and final displacement errors associated with predicting the trajectories of the player run up and the final kick. We conclude by extracting the visual and semantic features of the videos and testing with a multi-task prediction model in order to achieve a better result of trajectory prediction.