Many　semantic　events　in　team　sport　activities　e.g.　basketball　often　involve　both　group　activities　and　the　outcome　(score　or　not).　Motion　patterns　can　be　an　effective　means　to　identify　different　activities.　Global　and　local　motions　have　their　respective　emphasis　on　different　activities,　which　are　difficult　to　capture　from　the　optical　flow　due　to　the　mixture　of　global　and　local　motions.　Hence　it　calls　for　a　more　effective　way　to　separate　the　global　and　local　motions.　When　it　comes　to　the　specific　case　for　basketball　game　analysis,　the　successful　score　for　each　round　can　be　reliably　detected　by　the　appearance　variation　around　the　basket.　Based　on　the　observations,　we　propose　a　scheme　to　fuse　global　and　local　motion　patterns　(MPs)　and　key　visual　information　(KVI)　for　semantic　event　recognition　in　basketball　videos.　Firstly,　an　algorithm　is　proposed　to　estimate　the　global　motions　from　the　mixed　motions　based　on　the　intrinsic　property　of　camera　adjustments.　And　the　local　motions　could　be　obtained　from　the　mixed　and　global　motions.　Secondly,　a　two-stream　3D　CNN　framework　is　utilized　for　group　activity　recognition　over　the　separated　global　and　local　motion　patterns.　Thirdly,　the　basket　is　detected　and　its　appearance　features　are　extracted　through　a　CNN　structure.　The　features　are　utilized　to　predict　the　success　or　failure.　Finally,　the　group　activity　recognition　and　success/failure　prediction　results　are　integrated　using　the　kronecker　product　for　event　recognition.　Experiments　on　NCAA　dataset　demonstrate　that　the　proposed　method　obtains　state-of-the-art　performance.　(C)　2020　Elsevier　B.V.　All　rights　reserved.