Motion detection, line-crossing, object/person detection, and facial recognition capabilities, edge-versus-server-based analytics processing, and the false-alarm-rate trade-offs that determine whether an analytics deployment actually strengthens detection.
Video analytics is what converts CCTV from a passive recording tool into an active detect-layer technology, but every analytic still inherits the field-of-view, resolution, and lighting discipline covered in Module 2 — an analytic cannot detect what the underlying camera system cannot resolve. This module works through the analytics capability progression from basic motion detection through line-crossing and object classification to facial recognition, then covers where that processing actually happens and how false-alarm rate is managed in a real deployment.
By the end of this module you should be able to explain why a poorly tuned analytic that generates ten false alerts for every real event is not just inefficient but actively counterproductive to detection — a alarm-fatigue failure mode this module frames from first principles and Module 15's troubleshooting content returns to during commissioning.