Football activity recognition: Improving and testing football activity recognition based on signal data using deep learning.

There is a raising demand for player statistics in the world of football. With the developments over the last years in wearable sensors, Human Activity Recognition (HAR) based on wearable IMU sensors can be used to tackle this problem. This thesis builds upon an earlier research done for this topic, where an end-to-end pipeline based on deep...

An Artificial Neural Network Approach to Within-Game Predictions in Football using Spatiotemporal Data

Recent developments in \ac{ML} have paved the way for unprecedented possibilities in the field of data analytics in numerous team sports, such as American football, baseball, and basketball. In more recent years, \ac{ML} techniques have been applied to football as professional teams got inspired to collect enormous quantities of data to evaluate...

An End-to-End Deep Learning Pipeline for Football Activity Recognition Based on Wearable Acceleration Sensors

Action statistics in sports, such as the number of sprints and jumps, along with the details of the corresponding locomotor actions, are of high interest to coaches and players, as well as medical staff. Current video-based systems have the disadvantage that they are costly and not easily transportable to new locations. In this study, we...

Where to Score in AI World Cup Football?

To push the boundaries of technology, the world cup football for robots, RoboCup, is organized on a yearly basis since 1997. To push the boundaries of artificial intelligence, a simulated version of the RoboCup, AI World Cup Football, is arranged yearly from 2017. This requires skillful attackers, defenders and goalkeeper. A large part of having...

Football activity recognition: A deep learning approach to football activity recognition based on Inertial Measurement Units signals

been given to Human Activity Recognition (HAR) based on signals obtained by IMUs placed on different body parts. This thesis studies the usage of Deep Learning-based models to recognize different football activities in an accurate, robust, and fast manner. Several deep architectures were trained with data captured with IMU sensors placed on...