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CASE STUDY

PTZ Watch – Detecting Camera Movements on Traffic Surveillance

We developed a lightweight pipeline to detect pan, tilt, and zoom (PTZ) movements on traffic cameras, distinguishing real camera shifts from noise such as headlights, glare, and moving vehicles — even at night and across hundreds of streams simultaneously.

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Industry

Smart Cities & Traffic Monitoring

Deployment

Lightweight PTZ detection pipeline

Scope

Runs on hundreds of live camera streams

Core KPI

Reliable PTZ detection with low CPU usage

Overview

Traffic surveillance systems rely on fixed cameras for analytics like vehicle counting, congestion detection, or incident tracking. But when operators move cameras (pan, tilt, zoom), automated analytics break down.

The client needed a solution that could detect when PTZ movements happen, separate them from false alarms (headlights, reflections, rain), and log events for operational reliability — all with minimal CPU usage to scale across hundreds of cameras.

The Challenge

  • Real PTZ movements vs. false positives (headlights, glare, weather).

  • Nighttime environments with strong reflections and noise.

  • Low CPU budget per stream — solution must scale to 100+ cameras.

  • Need for clear audit logs: “Was there a PTZ in the last hour?”

Our Solution

We built a lightweight vision pipeline for PTZ detection:

  • Frame series analysis – comparing global scene changes (rotation, zoom, parallax) against local motion (cars, lights).

  • Sparse sampling – using anchor frames over time to reduce CPU usage without losing sensitivity.

  • Dual reference sets – training simple thresholds and rules from true PTZ events and false triggers.

  • Window-based outputs – per time window (e.g., 1 hour): PTZ yes/no, approximate interval, and confidence score.

  • Integration – events forwarded to the monitoring platform for automated responses.

Business Benefits

  • Reliable detection – distinguishes real PTZ events from noise.
  • Scalable performance – lightweight enough to run across hundreds of streams.
  • Operational safety – analytics can pause or reconfigure when PTZ is detected.
  • Full traceability – event logs with timestamp and confidence, supporting audits and continuous improvement.

How It Works

Input

Periodic still frames from traffic cameras.

Global vs. local change analysis

Detect large scene shifts vs. vehicle motion or light flashes.

Noise suppression

Special handling for headlights, glare, and nighttime flashes.

Decision logic

Classifier combines rules + thresholds to confirm PTZ.

Output

Log entry with PTZ status, time interval, and confidence level.

Technical Highlights

  • Uses computer vision to detect global scene movements (pan/tilt/zoom).

  • Nighttime optimization to filter out glare and light artifacts.

  • Smart frame sampling reduces CPU load per camera.

  • Distributed orchestration allows scaling across large camera networks.

Key Results

Reliable PTZ detection across day/night conditions.

Scalable solution running on hundreds of camera feeds.

Consistent event logs with timestamps and confidence.

Prepared for future expansion: detect type of movement (pan, tilt, zoom) and adapt thresholds by camera/time.

See It in Action

  • Service for PTZ detection with API/CLI outputs.
  • Dashboard with simple metrics and logs.
  • Integration with the client’s camera monitoring platform.
  • Library of labeled true PTZ vs. false triggers for model calibration.

Smarter Traffic Analytics with Reliable PTZ Detection

Ensure your traffic analytics remain reliable by detecting real camera movements and filtering out false alarms.

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