Motion　information　used　in　the　existed　video　action　recognition　schemes　is　mixing　of　global　motion(GM)　and　local　motion(LM).　In　fact,　GM　&　LM　have　their　respective　semantic　concepts.　Thus,　it　is　promising　to　decouple　GM　and　LM　from　the　mixed　motions.　Numerous　efforts　have　been　made　on　the　design　of　global　motion　models　for　video　encoding,　video　dejittering,　video　denoising,　and　so　on.　Nevertheless,　some　of　the　models　are　too　basic　to　cover　the　camera　motions　in　action　recognition　while　others　are　over-complicated.　In　this　paper,　we　focus　on　the　characteristic　of　the　action　recognition　and　propose　a　novel　independent　univariate　GM　model.　It　ignores　camera　rotation,　which　appears　rarely　in　action　recognition　videos,　and　represents　the　GM　in　x　and　y　direction　respectively.　Furthermore,　GM　is　position　invariant　because　it　is　from　the　universal　camera　motion.　Pixels　with　global　motions　are　subjected　to　the　same　parametric　model　and　pixels　with　mixed　motion　can　be　seen　as　outliers.　Motivated　by　this,　we　develop　an　iterative　optimization　scheme　for　GM　estimation　which　removes　the　outlier　points　step　by　step　and　estimates　global　motions　in　a　coarse-to-fine　manner.　Finally,　the　LM　is　estimated　through　a　Spatio-temporal　threshold-based　method.　Experimental　results　demonstrate　that　the　proposed　GM　model　makes　a　better　trade-off　between　the　model　complexity　and　the　robustness.　And　the　iterative　optimization　scheme　is　more　effective　than　the　existed　algorithms.　The　compared　experiments　using　four　popular　action　recognition　models　on　UCF-101　(for　action　recognition)　and　NCAA　(for　group　activity　recognition)　demonstrate　that　local　motions　are　more　effective　than　the　mixed　motions.　©　2021