A Proactive Approach to Safety: Leveraging Edge AI for Public Security

The Evolving Challenges of Securing Public Spaces

As public safety operations become more complex, traditional approaches to monitoring and incident management are increasingly constrained by limitations in responsiveness, consistency, and scalability. Systems are now expected to support real-time awareness, rapid decision-making, and continuous operational reliability.

In response, many organizations are reassessing how video surveillance and safety monitoring systems are designed and operated. Edge AI has emerged as a practical approach by enabling video data to be analyzed locally, reducing latency and supporting faster, more reliable detection of safety-related events.

Technology providers such as AVCiT are addressing these challenges by combining edge computing with IP-based system architectures for public security applications. This approach supports distributed and resilient monitoring frameworks, offering a practical path forward as safety operations become more dynamic and interconnected.

How AVCiT's Edge AI Plays a Critical Role in Safety

AVCiT's AI Box is an edge AI device designed for real-time video analysis across a wide range of operational environments, including safety-critical applications. It forms the foundation of AVCiT’s approach to video intelligence, enabling monitoring and event detection to be handled directly within the operating environment.

High-Accuracy, Multi-Algorithm Incident Detection

AVCiT's Edge AI solution continuously analyses live video streams rather than passively recording footage for post-incident review. By applying real-time video analytics, the system identifies safety-related events as they occur, supporting earlier intervention and more consistent monitoring across complex environments.

Each AI Box device supports up to 50  algorithms, with up to 8 algorithms running concurrently. This allows multiple safety scenarios, such as crowd conditions, fire risks, restricted access, and staff compliance, to be monitored at the same time without compromising system performance. As a result, operators can focus more on decision-making and response instead of constant visual observation.

Faster Response Through Automated Detection and Alerting

In safety-critical situations, even small delays can increase risk. Cloud-based analytics often introduce latency due to bandwidth limitations or network congestion. By processing video data locally at the edge, the AVCiT's AI Box system significantly reduces this delay.

Safety events are detected in real time, and alerts are generated and delivered on site without waiting for cloud processing. This enables near-instant notification to operators or connected systems, supporting faster response even during peak periods or in high-traffic environments.

System Resilience and Operational Continuity

Public safety systems must remain operational under all conditions. Centralized architectures can create single points of failure, where a server or network issue disrupts monitoring across multiple locations.

The AI Box nodes operate within a distributed architecture, where each device functions independently. If one unit fails, the rest of the system continues operating, allowing safety monitoring to remain active during partial outages. This design supports consistent performance across large deployments and is particularly important for environments that require uninterrupted coverage.

Scalable and Easy to Deploy

Scalability and deployment efficiency are key considerations for public-sector and commercial projects. Each AI Box unit supports input from up to 32 cameras and integrates with existing CCTV infrastructure, including IP cameras, NVRs, and DVRs.

Because the system does not require camera replacement or major structural changes, deployment time and cost are reduced. The plug-and-play design also allows additional cameras or AI devices to be added gradually as safety requirements evolve, without disrupting ongoing operations.

Improved Privacy and Data Protection

Data privacy is an increasing concern in public surveillance systems. Transmitting significant volumes of video data to external platforms can increase exposure to security risks and regulatory complexity.

By analyzing video data locally within a local area network, the edge AI platform reduces unnecessary data transmission and keeps sensitive information on site. This approach helps organizations strengthen data protection practices while aligning more closely with privacy and governance requirements.

Where AVCiT's AI Edge Technology Makes a Real Difference

Case Study Overview: MixC Shopping Malls

A practical example of Edge AI applied in a public environment can be seen in AVCiT's deployment across MixC shopping malls. The project involved a large-scale upgrade of existing surveillance systems, focused on improving safety monitoring and incident response without disrupting daily operations.

More than 3,000 surveillance cameras were integrated across MixC shopping centers in Shanghai, Shenzhen, and Shenyang. The deployment demonstrates how edge-based video analytics can be applied at scale in complex, high-traffic public venues.

Core Safety Applications in MixC Shopping Centers

*Crowd and Patron Safety
The system continuously monitors crowd density and pedestrian flow, enabling early detection of congestion, unsafe movement patterns, falls, and sudden disturbances. This allows on-site security teams to intervene proactively before situations escalate.

*Fire, Environmental, and Evacuation Safety
Edge AI analytics identify blocked emergency exits, fire door obstructions, indoor smoking, and other conditions that increase fire risk or hinder evacuation. Early alerts support faster corrective action and improve overall emergency readiness.

*Restricted Area Protection
AVCiT's AI Box detects unauthorized pedestrian access and vehicle movement in staff-only or hazardous areas such as renovation zones, loading docks, and service corridors. This reduces accident risks and improves compliance with safety regulations.

*Staff Safety and On-Duty Compliance
AI algorithms monitor staff behavior to identify situations such as leaving posts unattended, sleeping on duty, or unauthorized mobile phone use. These insights help maintain service quality and ensure rapid response capability during incidents.

Measured Results and Operational Impact

The deployment delivered measurable improvements across safety and efficiency metrics:

* Overall efficiency in handling safety hazard incidents increased by nearly 80% compared to manual inspections

* From incident occurrence to alert display, response times were consistently controlled within 20 milliseconds, even during peak hours

* Detection accuracy reached 99% for open flame and smoke, and 98% for unsafe behaviors such as smoking violations and staff absence

* The cost per video stream was up to ten times lower compared to AI-enabled camera solutions

These results demonstrate how Edge AI can scale across multi-site, complex environments while delivering both performance and cost efficiency.

Enabling Safer Environments for the Public

As public spaces continue to grow in size and complexity, safety systems must evolve beyond traditional surveillance models. Edge AI offers a practical, scalable, and privacy-conscious way to enhance public security while supporting human operators rather than replacing them.

By combining real-time intelligence, low-latency processing, system resilience, and seamless integration with existing infrastructure, AVCiT's Edge AI solution enables organizations to move from reactive monitoring to proactive safety management.

For shopping centers, transport hubs, campuses, and other public venues, Edge AI is no longer a future concept. It is a proven technology that delivers measurable improvements in safety, efficiency, and operational confidence, creating safer environments for everyone who uses them.

To better understand how distributed control room and edge AI solutions are applied in real-world public safety operations, contact AVCiT to discuss relevant use cases and system design considerations.

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