A spatiotemporal descriptor based on radial distances and 3D joint tracking for action classification
Action recognition is an important research area that is particularly challenging when dealing with view independent and unconstrained human motion. While progress has been made in developing pose-dependent action classification systems, the introduction of affordable 3D sensors has opened up opportunities for action classification with depth data. In this paper, we propose an efficient 3D descriptor combining radial distance measures on 2D video sequences with 3D joint tracking on depth data for action classification through Manifold Learning using supervised Locality Preserving Projections (sLPP). We find that the application of radial distances on depth data is effective at classifying actions and when combined with 3D joint tracking the action classification performance improves. We applied our method on the Microsoft Research 3D Dataset (MSR3D) and obtained good classification accuracy on all 20 unique 3D actions. Activity recognition rates were as high as 98.95% on subsets of 3D actions.