Existing　group　activity　recognition　methods　generally　use　optical　flow　image　to　represent　motion　within　videos,　which　often　struggle　to　capture　the　movements　of　individuals　inaccurately.　In　this　paper,　we　explore　the　effectiveness　of　more　kinds　of　motion　information　for　group　activity　recognition.　We　propose　a　novel　multi-　scale　MOtion-based　relational　reasoning　framework　for　Group　Activity　Recognition　(MOGAR).　It　combines　joint　motion　(intra-individual　level)　with　trajectory　(individual-level)　and　individual　position　(inter-individual　level)　to　acquire　richer　activity　representation.　Specifically,　it　involves　two　branches:　the　trajectory　branch　utilizes　individuals＇　trajectories　and　positions　to　extract　the　motion　feature　at　the　individual　and　inter-individual　levels.　The　joint　branch　extracts　the　motion　features　at　the　intra-individual　level.　Furthermore,　the　gated　recurrent　units　(GRU)　and　Transformers　are　employed　to　enhance　the　corresponding　features　through　gating　mechanism　and　self-attention　mechanism.　The　features　from　the　two　branches　are　concatenated　for　group　activity　recognition.　The　experiments　on　two　public　datasets　demonstrate　that　our　method　achieves　competitive　performance　and　has　potential　benefits　in　terms　of　computational　complexity.