system_design.md

AI Queue Management for CCTV and YOLO

System Architecture

The system consists of three main components:

1. Vision Engine: Uses YOLOv8 (via Ultralytics) and Roboflow Supervision to track people and calculate their "time in zone" (dwell time).
2. Log Analysis Engine: Uses Qwen-2.5-1.5B-Instruct (via Hugging Face Transformers) to process structured logs and provide actionable insights.
3. User Interface: A Gradio/Streamlit dashboard for real-time monitoring, log visualization, and AI-powered reporting.

Expanded Use Cases

Beyond basic queue monitoring, the system can be applied to:

• Retail Heatmap & Dwell Time: Identify which product sections attract the most customers and how long they stay.
• Bank Branch Efficiency: Analyze service times at different counters (as seen in the provided log) to optimize staffing.
• Airport Security Checkpoints: Predict wait times and alert staff to open new lanes before overflows occur.
• Hospital Emergency Rooms: Monitor patient waiting areas to ensure timely triage and care.
• Smart Parking: Track how long vehicles stay in specific zones to manage turnover and billing.
• Safety Monitoring: Detect if individuals stay too long in restricted or hazardous zones.

Log Data for LLM

The following structured data will be fed to the LLM for analysis:

• Branch/Location: Context for the analysis.
• Throughput: Total customers served.
• Wait Time Metrics: Average and maximum wait times.
• Service Efficiency: Average service time per counter.
• Peak Hours: Identification of the busiest periods.
• Anomaly Events: Queue overflow events or long wait time alerts.