Dynamic voxel carving in tennis based on player localisation using a low cost camera network

Abstract

In this paper, we address the problem of reconstructing 3D volumetric models, illustrating human sporting performance for use in coaching scenarios. We advocate the use of low cost camera networks for acquiring such data, allowing the approach to be feasibly adopted by both amateur and elite level sports athletes. A dynamic voxel carving approach is described, coupled with over-head player tracking and autonomous background subtraction, to automatically produce a 3D reconstruction technique that intelligently uses memory resources. We demonstrate the efficacy of our approach in the context of tennis as a challenging application scenario.

Designing a Topological Algorithm for 3D Activity Recognition

“…The general voxel technique proposed in [12] was modified and adapted to a specific task, as fully detailed in [16,17]. And it is, in fact, that specific voxel carving technique that we are using in this paper, fixing the number of cameras to five, since this is the usual constraint we can find in practise.…”

Topological evaluation of volume reconstructions by voxel carving

“…The technique involves capturing a series of synchronised images of an object, and, by analysis of these images and with prior knowledge of the exact threedimensional location of the cameras, deriving an approximation of the shape of the object. The general voxel technique proposed in [10] has been modified and adapted to a specific task, as fully detailed in [12,13]. And it is in fact this specific voxel carving technique that we are evaluating.…”

“…However, one of the fundamental difficulties with voxel carving is the unavailability of a ground truth from which to derive the accuracy of the technique. In an attempt to provide some quantitatively evaluation of voxel carving Monaghan et al [13] present an evaluation of the technique indirectly via silhouette comparison and in [12] present a method to quantitatively evaluate spatially carved volumetric representations of humans using a synthetic dataset of virtual humans in a tennis court scenario. Such quantification is based on a calculated ground truth and on the computation of Normalised Mean Square Error (NMSE).…”