On The Precision Of Markerless 3D Semantic Features: An Experimental Study On Violin Playing

Human motion analysis is an essential task in several domains and, depending on the application field, it requires different level of accuracy. In the motor control field it is commonly performed with motion capture systems and infrared markers that guarantee a high accuracy. However, these systems are expensive, cumbersome, and may induce bias. An alternative to marker-based technologies are image-based marker-less systems, that are cheaper and do not affect the naturalness of the motion. Although their accuracy level seems to limit their use in motor control field, a thorough quantitative comparison with marker-based techniques does not appear to be available yet. We compare the estimates of a 3D image-based marker-less pipeline we propose, with a standard marker-based system; the analysis is carried out on a multi-sensor dataset acquired to study the motion of violin players. The results we obtain on the precision level are suggesting that marker-less systems may successfully track performances in real-world settings.