Video　anomaly　detection　is　a　crucial　topic　in　the　field　of　deep　learning.　Typically,　manually　monitoring　abnormal　behavior　in　surveillance　videos　is　a　labor-intensive　task.　As　society　advances,　the　significance　of　video　anomaly　detection　continues　to　grow.　Inspired　by　reconstruction　methods　and　frame　extraction　for　constructing　pseudo　anomalies　in　anomaly　detection,　we　propose　an　autoencoder-based　anomaly　detection　model.　In　this　model,　normal　samples　are　represented　as　average　images,　and　anomaly　regions　are　highlighted　by　whitening.　The　model　offers　several　advantages:　1)　It　significantly　reduces　the　construction　of　anomalies　in　normal　parts　compared　to　previous　reconstruction　methods,　as　there　is　no　need　to　reconstruct　foreground　content.　2)　Anomaly　regions　are　clearly　highlighted　by　whitening.　3)　The　model　exhibits　high　sensitivity　to　individuals　and　objects　with　abnormal　speeds　in　the　scene,　enabling　easy　identification.　4)　Overlaying　normal　and　pseudo-anomalous　samples　effectively　addresses　issues　related　to　occlusion　of　normal　parts　and　insufficient　emphasis　on　anomalous　regions.　We　conducted　tests　on　the　USCD　Ped2　anomaly　detection　dataset,　achieving　a　micro　AUC　of　99.6%.　Our　model　demonstrates　competitiveness　compared　to　state-of-the-art　models.　©　2023　IEEE.