Optimizing Long-Term Player Tracking and Identification in NAO Robot Soccer by fusing Game-state and External Video

Giuliano Albanese, Arka Mitra, Jan-Nico Zaech, Yupeng Zhao, Ajad Chhatkuli, Luc Van Gool

Keywords: Tracking, RoboCup, Game Statistics

TL;DR: A tracking method to fuse information from external camera and sensors on robotic players. Aimed for analyzing RoboCup soccer games.

Abstract: Monitoring a fleet of robots requires stable long-term tracking with re-identification, which is yet an unsolved challenge in many scenarios. One application of this is the analysis of autonomous robotic soccer games at RoboCup. Tracking in these games requires handling of identically looking players, strong occlusions, and non-professional video recordings, but also offers state information estimated by the robots. In order to make effective use of the information coming from the robot sensors, we propose a robust tracking and identification pipeline. It fuses external non-calibrated camera data with the robots' internal states using quadratic optimization for tracklet matching. The approach in this work is validated using game recordings from previous RoboCup World Cups.

Submission Number: 9