Group　activity　recognition　aims　to　recognize　behaviors　characterized　by　multiple　individuals　within　a　scene.　Existing　schemes　rely　on　individual　relation　inference　and　usually　take　the　individuals　as　tokens.　Essentially　they　select　the　most　relevant　region　of　the　group　activity　from　the　entire　image　while　filtering　out　irrelevant　background　noises.　However,　these　schemes　require　individual　bounding　box　labeling　in　both　training　and　testing　stages.　Since　individuals　have　usually　been　presented　at　one　scale,　multi-scale　individuals　cannot　be　combined　in　an　effective　way.　In　this　paper,　we　present　a　novel　end-to-end　hierarchical　relation　inference　framework　based　on　active　spatial　positions　for　group　activity　recognition.　This　framework　is　designed　to　locate　active　spatial　positions　and　use　them　as　visual　tokens　to　infer　the　relations　for　token　embeddings.　It　requires　individual　bounding　box　labeling　only　in　the　training　stage　while　automatically　eliminating　the　background　after　locating　active　spatial　positions　from　the　entire　scene.　The　hierarchical　relations　can　be　naturally　inferred　based　on　the　visual　tokens　at　different　scales,　contributing　to　further　performance　improvement.　Experimental　results　demonstrate　that　the　proposed　framework　is　competitive　against　existing　schemes　that　require　more　laboring　and　computation　to　generate　labels　in　both　the　training　and　testing　stage.　IEEE