Background Modelling on Tensor Field for Foreground Segmentation

The paper proposes a new method to perform foreground detection by means of background modeling using the tensor concept. Sometimes, statistical modelling directly on image values is not enough to achieve a good discrimination. Thus the image may be converted into a more information rich form, such as a tensor field, to yield latent discriminating features. Taking into account the theoretically well-founded differential geometrical properties of the Riemannian manifold where tensors lie, we propose a new approach for foreground detection on tensor field based on data modeling by means of Gaussians mixtures directly on tensor domain. We introduced a online Kmeans approximation of the Expectation Maximization algorithm to estimate the parameters based on an Affine-Invariant metric. This metric has excellent theoretical properties but essentially due to the space curvature the computational burden is high. We propose a novel Kmeans algorithm based on a new family of metrics, called Log-E...

• Type: Journal
• Year: 2010
• Where: BMVC
• Authors: Rui Caseiro, Jorge Batista, Pedro Martins
• Keywords: BMVC 2010, Computer Vision, Tensor Field, Tensors, Theoretical Properties