<p>Detecting abnormal events in videos is essential for effective anomaly detection in surveillance environments. In this work, we propose a framework that combines the Abnormality-Aware Fused Attention Model (AAFAM) with the Global Density Joined Network (GDJNet) to enhance abnormal event detection. AAFAM uses spatial and channel-wise attention mechanisms to focus on anomalous regions in feature maps. It suppresses irrelevant background information. GDJNet, on the other hand, captures both local and global spatial relationships through density estimation. It allows the model to learn object distributions and co-occurrence patterns at multiple scales. To strengthen the model’s performance, we integrate AAFAM and GDJNet using a fusion strategy that combines attention and density maps, resulting in highly discriminative feature representations. Experimental results show that the proposed AAFAM–GDJNet framework outperforms existing methods and achieves state-of-the-art performance.</p>