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.