From canonical poses to 3D motion capture using a single camera.
We combine detection and tracking techniques to achieve robust 3D motion recovery of people seen from arbitrary viewpoints by a single and potentially moving camera. We rely on detecting key postures, which can be done reliably, using a motion model to infer 3D poses between consecutive detections, and finally refining them over the whole sequence using a generative model. We demonstrate our approach in the cases of golf motions filmed using a static camera and walking motions acquired using a potentially moving one. We will show that our approach, although monocular, is both metrically accurate because it integrates information over many frames and robust because it can recover from a few misdetections.
Fossati
Andrea
A
Ecole Polytechnique Fédérale de Lausanne(EPFL)/IC/ISIM/CVLab, Computer Vision Laboratory, I&C Faculty,Station 14, CH-1015 Lausanne, Switzerland. andrea.fossati@epfl.ch
Dimitrijevic
Miodrag
M
Lepetit
Vincent
V
Fua
Pascal
P
D000465:Algorithms; D001185:Artificial Intelligence; D006055:Golf; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009038:Motion; D016011:Normal Distribution; D011187:Posture; D014741:Video Recording / Q000379:methods*; D016138:Walking