Anomaly　detection　in　video　surveillance　is　challenging　due　to　the　variety　of　anomaly　types　and　definitions,　which　limit　the　use　of　supervised　techniques.　As　such,　auto-encoder　structures,　a　type　of　classical　unsupervised　method,　have　recently　been　utilized　in　this　field.　These　structures　consist　of　an　encoder　followed　by　a　decoder　and　are　typically　adopted　to　restructure　a　current　input　frame　or　predict　a　future　frame.　However,　regardless　of　whether　a　2D　or　3D　autoencoder　structure　is　adopted,　only　single-scale　information　from　the　previous　layer　is　typically　used　in　the　decoding　process.　This　can　result　in　a　loss　of　detail　that　could　potentially　be　used　to　predict　or　reconstruct　video　frames.　As　such,　this　study　proposes　a　novel　spatio-temporal　U-Net　for　frame　prediction　using　normal　events　and　abnormality　detection　using　prediction　error.　This　framework　combines　the　benefits　of　U-Nets　in　representing　spatial　information　with　the　capabilities　of　ConvLSTM　for　modeling　temporal　motion　data.　In　addition,　we　propose　a　new　regular　score　function,　consisting　of　a　prediction　error　for　not　only　the　current　frame　but　also　future　frames,　to　further　improve　the　accuracy　of　anomaly　detection.　Extensive　experiments　on　common　anomaly　datasets,　including　UCSD　(98　video　clips　in　total)　and　CUHK　Avenue　(30　video　clips　in　total),　validated　the　performance　of　the　proposed　technique　and　we　achieved　96.5%　AUC　for　the　Ped2　dataset,　which　is　much　better　than　existing　autoencoder-based　and　U-Net-based　methods.