Real-Time First Person Perspective Tracking and Feedback System for Weapon Practice Support in Fencing
Malawski Filip · Frontiers in artificial intelligence and applications · 2018
Current progress in technology enables high-level automatic analysis of human motion. Next to action and gesture recognition, sport analysis is one of active research subjects in this area. In many disciplines athletes benefit from introducing technology as a support of the training process. In this work we present a novel system supporting weapon practice in fencing. Active markers mounted on the weapon as well as first-person perspective camera are employed, in order to allow for accurate blade trajectories and rotation tracking. With the help of fencing experts, data are collected to train models of how specific actions should be performed. Based on these models our system is able to assess how accurately given action is performed by a practising person. Feedback is delivered in real-time via visualization of trajectories as well as by numerical measures. The proposed system was evaluated in a number of experiments as well as by fencers and a fencing coach.