Ball tracking by object detection using deep neural network aided Kalman filtering in a real-time simulated RoboCup Soccer environment

Abstract

Computer vision has the ability to provide an abundance of environmental information to a robotic system, which makes perception and interaction with a dynamic world possible. In this research report the problem of real-time ball tracking in the context of simulated RoboCup soccer is considered. A tracking-by-detection framework is utilized to take advantage of the high performance of modern neural detection techniques as well as the use of neural assisted Kalman filtering for tracking which strikes a balance between the ease of interpretation of the traditional model-based approach and the ability to learn complex dynamics using neural networks. The results show that a modern keypoint based detector can outperform established real-time RoboCup ball detection techniques such as traditional Viola-Jones based detection as well as popular anchor based detection approaches, in the context of simulated RoboCup soccer. Further, a neural approach to Kalman filtering is able to outperform the popular extended Kalman filter for simulated RoboCup soccer kick tracking by implicitly learning the system dynamics.