Rapid　and　accurate　detection　of　crowd　abnormal　events,　such　as　stampedes　and　violent　attacks　in　public　places,　has　great　research　significance　and　application　value.　Due　to　the　diversity　and　uncertainty　of　abnormal　events,　almost　all　existing　methods　tackle　the　problem　by　minimizing　the　reconstruction　errors　of　training　data,　which　cannot　guarantee　a　larger　reconstruction　error　for　all　abnormal　events.　According　to　the　idea　that　＇normal　events　can　be　predicted,　abnormal　events　cannot　be　predicted,＇we　proposed　a　future　frame　prediction-based　anomaly　detection　algorithm.　First,　the　generative　adversarial　network　(GAN)　is　trained　by　the　normal　videos　to　predict　normal　future　frames.　Then,　it　can　determine　the　existence　of　abnormal　events　by　identifying　the　difference　between　the　ground　truth　and　predicted　video　frame.　In　the　design　of　the　GAN,　the　attention　module　is　introduced　to　improve　the　prediction　level　of　the　network.　At　the　same　time,　the　optical　flow　information　is　added　for　motion　constraint　to　improve　the　constraint　ability　on　the　appearance　characteristics.　In　the　testing　stage,　the　appearance　gap　and　optical　flow　gap　between　the　ground　truth　and　the　predicted　video　frame　are　fused　to　determine　whether　the　frame　is　abnormal.　The　experimental　results　on　the　datasets　of　CUHK　Avenue,　UCSD,　and　ShanghaiTech　show　that　the　proposed　algorithm　is　superior　to　that　of　the　current　mainstream　anomaly　detection　algorithms.　©　2021　SPIE　and　IS&T.