In　multicamera　video　surveillance,　it　is　challenging　to　represent　videos　from　different　cameras　properly　and　fuse　them　efficiently　for　specific　applications　such　as　human　activity　recognition　and　clustering.　In　this　paper,　a　novel　representation　for　multicamera　video　data,　namely,　the　product　Grassmann　manifold　(PGM),　is　proposed　to　model　video　sequences　as　points　on　the　Grassmann　manifold　and　integrate　them　as　a　whole　in　the　product　manifold　form.　In　addition,　with　a　new　geometry　metric　on　the　product　manifold,　the　conventional　low　rank　representation　(LRR)　model　is　extended　onto　PGM　and　the　new　LRR　model　can　be　used　for　clustering　nonlinear　data,　such　as　multicamera　video　data.　To　evaluate　the　proposed　method,　a　number　of　clustering　experiments　are　conducted　on　several　multicamera　video　data　sets　of　human　activity,　including　the　Dongzhimen　Transport　Hub　Crowd　action　data　set,　the　ACT　42　Human　Action　data　set,　and　the　SKIG　action　data　set.　The　experiment　results　show　that　the　proposed　method　outperforms　many　state-of-the-art　clustering　methods.