Due　to　the　rapid　growth　of　modern　mobile　devices,　users　can　capture　a　variety　of　videos　at　anytime　and　anywhere.　The　explosive　growth　of　mobile　videos　brings　about　the　difficulty　and　challenge　on　categorization　and　management.　In　this　paper,　we　propose　a　novel　approach　to　annotate　group　activities　for　mobile　videos,　which　helps　tag　each　person　with　an　activity　label,　thus　helping　users　efficiently　manage　the　uploaded　videos.　To　extract　rich　context　information,　we　jointly　model　three　co-existing　cues　including　the　activity　duration　time,　individual　action　feature　and　the　context　information　shared　between　person　interactions.　Then　these　appearances　and　context　cues　are　modeled　with　a　structure　learning　framework,　which　can　be　solved　by　inference　with　a　greedy　forward　search.　Moreover,　we　can　infer　group　activity　labels　of　all　the　persons　together　with　their　activity　durations,　especially　for　the　situation　with　multiple　group　activities　co-existing.　Experimental　results　on　mobile　video　dataset　show　that　the　proposed　approach　achieves　outstanding　results　for　group　activity　classification　and　annotation.