Machine learning
Video detection & crowd alerts
Video detection summaries, persistent crowd-event alerts, and a comparison of lightweight object-detection models.
Overview
Making video activity easier to interpret
This project explores how object detection can turn video into summaries and event alerts. It identifies what appears in sampled frames and looks for a crowd that persists over time, rather than reacting to one isolated detection.
The work also compares lightweight detection models, exploring the practical balance between inference speed and the information returned.
Technical detail
YOLO and OpenCV support frame sampling and object detection. The crowd-event rule requires at least three people across five consecutive analysed frames, introducing a temporal condition before an alert is raised.
A separate comparison runs two YOLO variants against the same image set and records inference time, detection counts, and class diversity. Docker packages that workflow. These are experimental implementations, with notebooks and plots showing the individual tasks.