3d skeleton reconstruction from images using volumic probability data

Abstract

To generate 3D skeletons of a scene volume, a first approach retrieves probabilities from source images capturing the scene to detect parts of the objects, next merges the probabilities in 3D space. The merged or joint probabilities are then used to robustly detect the parts of the objects in the 3D space. This approach advantageously reduces complexity of the 3D skeleton reconstruction. In particular, at 2D level (i.e. on the source images), it avoids conflict resolutions to be performed between conflicting parts detected from different source images. It turns that real time reconstructions and subsequent displays or human motion detection are better achieved. The present approach efficiently works on complex scenes like sport events with multiple players in a stadium, with an ability to detect a wide number of interoperating 3D objects like multiple players.