Real-Time Intelligent Video Analytics System for Automated Surveillance, Facial Identification, and Human Activity Monitoring

Rapid urbanisation and the growth of smart city infrastructure have put heavy pressure on traditional surveillance

systems, which were never designed to handle current volumes and types of threats.[file:1] Conventional CCTV still relies on

human operators to continuously watch multiple screens, flag incidents, and raise alarms, which does not scale to large

camera networks.[file:1] Human attention typically drops after about twenty minutes of continuous monitoring, and feeds from

hundreds of cameras cannot realistically be reviewed in real time.[file:1] Recorded footage also lacks structured metadata, so it

cannot be searched or analysed automatically.[file:1]

To address these limitations, we built the Real-Time Intelligent Video Analytics System (RIVAS), which integrates three deeplearning models into a single, end-to-end pipeline.[file:1] YOLOv8 is used to locate people in each video frame, DeepFace

with an ArcFace backbone identifies who they are, and MediaPipe Holistic estimates body posture to infer activities.[file:1] As

each frame arrives, RIVAS detects all visible persons, checks whether their faces match an enrolled gallery, determines whether

the observed activity is a fall, an unauthorised intrusion, loitering, or normal movement, and immediately pushes a structured

alert via a Flask REST API to a Streamlit dashboard.[file:1] On a mixed indoor-outdoor dataset, the system achieved 75%

precision for person detection, 95% for face identification, and 94% for activity classification while sustaining 24 frames per

second at 720p resolution.[file:1] These results compare favourably with single-module systems and conventional CCTV

installations and represent a practical step toward more autonomous, real-time surveillance.[file:1]