Smart Surveillance
for Port Security with Vision Analytics
A private maritime logistics and security provider replaced manual CCTV monitoring with an edge-optimized AI vision system — delivering 24/7 surveillance with over 95% detection accuracy, near-instant alerting (under 2.3s latency), and 50% reduction in manual oversight.
Discuss Your Project →A maritime logistics provider held back by manual CCTV monitoring
A private maritime logistics and security provider needed a smarter way to monitor restricted access areas across large port environments. Their previous CCTV surveillance systems relied heavily on manual staff observation, making them prone to human error and delayed response times. We As Web was engaged to develop and deploy a real-time, AI-powered surveillance system capable of detecting people, vehicles, and suspicious motion across multiple camera feeds, maintaining reliability even in low-light and harsh coastal conditions.
What prompted the project
The client required a robust security solution that could automate anomaly detection, alert in real-time, and survive the realities of port operations.
Automate Anomaly Detection
Automatically detect intrusions, unauthorized movements, and vehicle entries — not just motion in general.
Real-Time Alerting
Generate alerts with minimal latency — under 2-3 seconds from event to notification.
Edge Deployment
Operate effectively on low-power edge devices located within the port infrastructure.
Seamless Integration
Connect seamlessly with the port's existing security dashboards — no parallel operations.
Environmental Resilience
Ensure functionality in highly variable conditions, such as fog, nighttime, and camera shake.
Why an expert partner was required
The project team had to navigate several significant technical and environmental constraints — particularly around compute, bandwidth, and the environment itself.
What We As Web delivered
We As Web delivered a comprehensive, edge-optimized vision solution divided across three main layers — model, integration, and intelligence.
Computer Vision & Object Classification
A PyTorch-based model trained to distinguish cargo, vehicles, humans, and static objects under diverse conditions.
Live Video Integration
WebRTC routes live camera feeds with near-zero latency, paired with a Flask API for dashboard integration.
Anomaly Scoring & Alerting Logic
AI-based rules engine assigns a specific threat score based on motion patterns, entry zones, and time of activity.
24/7 surveillance with over 95% accuracy and half the labor
The deployment of the smart surveillance system significantly upgraded the port's security operations — across accuracy, latency, and labor efficiency.
Achieved an object detection accuracy of over 95%, maintaining this high performance even in low-light conditions.
Cut manual surveillance labor by half, allowing the client to reallocate their staff to higher-value security tasks.
Enabled real-time alerting with an average latency of under 2.3 seconds — fast enough to matter.
The entire system was successfully integrated into the client's existing security infrastructure within just 4.5 months.
Edge-optimized vision for harsh port conditions
A focused, edge-first stack — PyTorch + OpenCV for the model, WebRTC for low-latency video, Flask for the API, MongoDB for state, and NVIDIA Jetson for the hardware.
When port security can't rely on humans staring at screens
A maritime logistics provider relying on manual CCTV monitoring is one operator distraction away from missing a critical intrusion. By building an edge-optimized AI vision system with PyTorch and OpenCV, routing live video with WebRTC, and layering AI-based anomaly scoring on top, we gave this port a 24/7 surveillance capability that outperforms what human eyes can sustain — even in fog, at night, and with shaky camera mounts.
The result is over 95% detection accuracy, under 2.3s alerting latency, 50% less manual oversight, and a 4.5-month integration into the existing security infrastructure. For any port, logistics hub, or critical infrastructure operator held back by manual monitoring, this is what edge AI vision looks like in production.