Anomaly Detection in Videos has gained interest in recent years due to a combination of factors, such as data availability, computational capabilities and deep learning techniques that automated the process. However, being able to process real-time video streaming on edge devices with limited resources is still an active area of research. To address this challenge, this thesis proposes quantized S3D-inspired architecture that employs MobileNet for initial 2D feature extraction followed by MoViNet for processing spatio-temporal features. The model is specifically optimized for Raspberry Pi’s computational constraints in conjunction with the Raspberry Pi AI Camera (IMX500) and employs post-training static quantization and quantization-aware training (QAT) to achieve over 79% frame-level AUROC and over 88% video-level AUROC on UCF-Crime.