Intelligent Video Surveillance and Anti‑Terrorism Techniques for Smart Cities

On April 3, 2017, a metro bombing in Saint Petersburg caused severe casualties, and authorities classified it as a terrorist attack, highlighting the growing global focus on counter‑terrorism since 9/11.

In the development of safe and smart cities, intelligent high‑definition video monitoring and analysis are the first essential steps for anti‑terrorism security.

By analyzing video content with preset alarm rules for different camera scenes, systems can automatically detect abnormal behavior, generate alerts, and produce statistical data from massive datasets, evolving visual monitoring into fully automated control. Public venues such as airports, train stations, subways, and bus stations are high‑risk areas that require focused surveillance.

The anti‑terrorism approach is divided into three stages: pre‑warning, in‑process response, and post‑event evidence collection.

Pre‑warning

Many violent terrorist incidents are organized and pre‑planned; early detection of suspicious signs can trigger emergency plans and reduce damage.

1. Personnel Monitoring – Deploy facial‑recognition checkpoints at entrances of train stations, bus stations, subways, etc., capturing attributes such as gender, age, height, clothing, glasses, and ethnic features. Matching captured faces against a blacklist generates automatic alerts to quickly lock down suspects.

2. Traffic Monitoring – Install vehicle checkpoints near public transport hubs to detect abnormal vehicle behavior (loitering, sudden acceleration, speeding, illegal entry). The system captures license plates, vehicle colors, models, and driver faces for later investigation.

3. Abnormal Behavior Monitoring – Intelligent cameras detect actions like drawing a weapon, running, fighting, or crowd surges by analyzing person count, movement speed, trajectories, and body posture, then trigger alarms and notify security personnel for rapid intervention.

In‑process Response

Fixed camera points provide blind‑spot‑free coverage of key locations, but mobile object detection is challenging. Wireless video monitoring systems using smart analytics enable real‑time tracking of incidents, improving command efficiency and response speed.

Post‑event Evidence

Rapid reconstruction of the scene after an incident is essential for criminal investigation. Intelligent video summarization tools help investigators extract useful clues from massive footage.

Video Management Software (VMS) Overview

VMS combines video management (ingest, playback, distribution, indexing of various streams) and video analytics (deep analysis of stored footage to uncover valuable big‑data insights). Its architecture typically consists of a presentation layer, an application layer, and a business layer.

Presentation Layer – Provides a web‑based interactive interface for customers and third‑party integration via SDKs.

Application Layer – Delivers business functions such as evidence (video, text) management, image repository, real‑time behavior analysis (perimeter intrusion, line crossing, loitering, object removal, direction, speed, path, head count, crowd density), and virtual checkpoint deployment.

Business Layer – Offers PaaS SDKs for secondary development, exposing core services like storage, databases, distributed computing, logging, and web capabilities. Modern VMS runs on X86 servers with scalable SAN storage and supports multi‑site disaster recovery.

In smart‑city security, VMS integrated with big‑data platforms enables real‑time behavior analysis, automatic alerts, and proactive incident prevention.

Commonly used behavior‑feature detection techniques include:

Line‑Crossing Detection

Sets virtual lines and directions; the system automatically detects crossing events and generates alerts.

Perimeter Intrusion

Defines a virtual perimeter; any breach triggers an alarm, allowing a single operator to monitor many cameras efficiently.

Object Removal

Detects when designated items are taken from a monitored area, issuing immediate warnings.

Object Left Behind

Identifies abandoned objects (potential explosives) in restricted zones and raises alerts.

Crowd Density Detection

Monitors the number of people in a region; exceeding preset thresholds triggers alarms for crowd control.

Speed Detection

Detects vehicles moving faster or slower than allowed, supporting traffic order enforcement.

As smart‑city and security projects mature, pre‑warning remains the most effective method to counter terrorism, laying the foundation for rapid response and evidence collection through advanced technology and relevant regulations.

Warm Tip: Search for "ICT_Architect" or scan the QR code below to follow the public account for more content.