Deep Learning for Surveillance AI

Train production-grade neural networks for real-time surveillance — object detection, tracking, and classification.

Advanced 2h 47m 4 lessons Laith Al-Haware English

About this course

Go from zero to deploying deep learning models that run 24/7 on real camera feeds. Covers YOLO, tracking algorithms, model optimization, and edge deployment strategies used in citywide surveillance systems.

What you'll learn

Train YOLO models on custom datasets

Optimize models with TensorRT for real-time inference

Implement multi-object tracking across cameras

Deploy models to production with monitoring

Curriculum

4 lessons · 2h 47m total · Preview lessons marked below

01

Introduction to Surveillance AI

The landscape, the stakes, and the stack.

Preview 12m
02

Dataset Preparation for YOLO

Annotation, augmentation, and class balancing.

48m
03

Training YOLOv8 from Scratch

Full training pipeline on custom data.

1h 2m
04

Evaluation & Iteration

mAP, precision-recall, and improving your model.

45m