In this paper we address the problem of modelling and analyzing human motion by focusing on 3D body skeletons. Particularly, our intent is to represent skeletal motion in a geometric and e cient way, leading to an accurate action-recognition system. Here an action is represented by a dynamical system whose observability matrix is characterized as an element of a Grassmann manifold. To formulate our learning algorithm, we propose two distinct ideas: (1) In the first one we perform classification using a Truncated Wrapped Gaussian model, one for each class in its own tangent space. (2) In the second one we propose a novel learning algorithm that uses a vector representation formed by concatenating local coordinates in tangent spaces associated with di↵erent classes and training a linear SVM. We evaluate our approaches on three public 3D action datasets: MSR-action 3D, UT-kinect and UCF-kinect datasets; these datasets represent di↵erent
To represent skeletal motion in a geometric, a process was presented by Salma et al. REF whose observability matrix was characterized as an element of a Grassmann manifold.
Accurate 3D action recognition using learning on the Grassmann manifold
Pattern Recognit.
Pattern Recognit.
Mathematics Computer Science