Shareability-Exclusivity Representation on Product Grassmann Manifolds for Multi-camera video clustering

With the rapid popularity of multi-camera networks, one human action is usually captured by multiple cameras located at different angles simultaneously. Multi-camera videos contain the distinct perspectives of one action, therefore multiple views can overcome the impacts of illumination and occlusion. In this paper, we propose a novel multi-camera video clustering model, named Shareability-Exclusivity Representation on Product Grassmann Manifolds (PGM-SER), to address two key issues in traditional multi-view clustering methods (MVC): (1) Most MVC methods directly construct a shared similarity matrix by fusing multi-view
data or their corresponding similarity matrices, which ignores the exclusive information in each view; (2) Most MVC methods are designed for multi-view vectorial data, which cannot handle the nonlinear manifold structure hidden in multi-camera videos. The proposed PGM-SER firstly adopts Product Grassmann Manifolds to represent multi-camera videos, then simultaneously learn their shared and exclusive information in global structures to achieve multi-camera video clustering. We provide an effective optimization algorithm to solve PGM-SER and present the corresponding convergence analysis. Finally, PGM-SER is tested on three multi-camera human action video datasets and obtain satisfied experimental results.