I recently built a real-time video anomaly detection system that classifies footage as violent or non-violent using transfer learning - and wanted to share what I learned, including the environment issues that ate up more time than the actual model building.

The Problem

Crowd monitoring systems today mostly rely on manual observation - someone watching camera feeds and reacting after something's already gone wrong. I wanted to build something that could flag anomalous behavior automatically, in real time, without needing a massive custom-trained model from scratch.

The Approach

Rather than training a CNN from zero (which needs huge datasets and compute I didn't have), I used transfer learning with MobileNetV2, pretrained on ImageNet:

Froze the base MobileNetV2 layers to keep the pretrained visual features intact

Added a custom head: GlobalAveragePooling2D → Dense(512, relu) → Dropout(0.5) → Dense(1, sigmoid)

Trained as a binary classifier: violent vs.…