Human　group　activity　recognition　(GAR)　has　attracted　significant　attention　from　computer　vision　researchers　due　to　its　wide　practical　applications　in　security　surveillance,　social　role　understanding　and　sports　video　analysis.　In　this　paper,　we　give　a　comprehensive　overview　of　the　advances　in　group　activity　recognition　in　videos　during　the　past　20　years.　First,　we　provide　a　summary　and　comparison　of　11　GAR　video　datasets　in　this　field.　Second,　we　survey　the　group　activity　recognition　methods,　including　those　based　on　handcrafted　features　and　those　based　on　deep　learning　networks.　For　better　understanding　of　the　pros　and　cons　of　these　methods,　we　compare　various　models　from　the　past　to　the　present.　Finally,　we　outline　several　challenging　issues　and　possible　directions　for　future　research.　From　this　comprehensive　literature　review,　readers　can　obtain　an　overview　of　progress　in　group　activity　recognition　for　future　studies.