Real-time marker-free motion capture system using blob feature analysis

Abstract

We developed blob feature analysis-based real-time marker-free motion capture system. Our system can capture actor’s end-effectors and reconstruct 3-dimensional human motions in real-time without any attaching markers or sensors. To capture robustly various motions of an actor, we proposed blob feature models such as shape model, color model, and spatial model for end-effectors such as a head, hands, and feet. And we introduce weights for each model. According to the clothing conditions of an actor, the proposed method adjusts weights for each model automatically. So, our system can detect and distinguish the actor’s end-effectors although the shapes and the color of end-effectors vary due to various poses and the variation of illumination. And our models are very simple to compute, therefore, the motion capture can be real-time process. Experiments are conducted on a lot of people wearing various clothes under general fluorescent lights. The proposed system can reconstruct actor’s various motions at 30 frames per second with the 99.95% success rate of the detection of an actor’s end-effectors. So, we confirmed that the proposed motion capture system could stably reconstruct motions of a lot of people wearing various clothes in real-time.