We present a novel system for anomaly detection in surveillance videos, specifically focusing on identifying instances where individuals deviate from public health guidelines during the pandemic. These anomalies encompassed behaviours like the absence of face masks, incorrect mask usage, coughing, nose-picking, sneezing, spitting, and yawning. Monitoring such anomalies manually was challenging and prone to errors, necessitating automated solutions. To address this, a multi-attention-based deep learning system was employed, utilizing the EfficientNet-B0 architecture. EfficientNet-B0, featuring the Mobile Inverted Bottleneck Convolution (MBConv) block with Squeeze-and-Excitation (SE) modules, emphasizes informative channel characteristics while disregarding irrelevant ones. However, this approach neglected crucial spatial information necessary for visual recognition tasks. To improve this, the Convolutional Block Attention Module (CBAM) was integrated into EfficientNet-B0 to improve feature extraction. The baseline EfficientNet-B0 model’s SE module was replaced with the CBAM module within each MBConv module to retain spatial information related to anomaly activities. Additionally, the CBAM module, when embedded after the second convolutional layer, was observed to significantly enhance the classification ability of the model across different anomaly classes, resulting in a significant accuracy boost from 87 to 96%. In conclusion, we demonstrated the efficacy of the CBAM module in refining feature extraction and improving the classification performance of the proposed method, showcasing its potential for robust anomaly detection in surveillance videos.