Edge vs Cloud AI for Traffic and Safety Solutions

In today’s rapidly urbanizing world, city authorities face mounting pressure to improve traffic safety, optimize infrastructure, and do more with fewer resources. The traditional, centralized approach to surveillance and data processing is giving way to a new generation of intelligent systems - built on the edge.

Edge AI is no longer a futuristic concept. It’s already reshaping how cities manage vehicle movement, monitor traffic violations, and enforce safety protocols. Unlike legacy systems that rely heavily on backend servers, edge-enabled devices now perform real-time recognition and analysis directly within the camera. The implications for public safety, responsiveness, and operational efficiency are profound.

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As Jan Hazlbauer, Chief Product Officer at FF Group, emphasized,

“Modern AI cameras are already fast and powerful enough for complex high-speed, multi-lane recognition.”

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FF Group’s AI edge apps illustrate this shift. Integrated directly into top open-platform cameras, it enables automatic license plate recognition and make-model-color classification at the edge. CAMMRA AI also supports radar integration for speed detection, reducing network loads and lowering total cost of ownership for municipalities.

But edge AI alone isn’t a one-size-fits-all solution. Christian Chenard-Lemire, Product Group Director at Genetec, underlined the importance of hybrid strategies:

“Edge provides continuous, low-cost analysis, but when you need to fuse data across devices or push frequent updates to AI models, that’s where the cloud shines.”

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In fact, Genetec’s portfolio includes cloud-native solutions like their Cloudrunner platform and SharpV cameras - which demonstrate how Edge and Cloud can complement each other. Hanwha is also investing in hybrid innovation with their new OnCloud platform, which extends their edge apps like RoadAI or HN Analytics suite to enable real-time recognition of vehicle attributes and behavior directly within the camera.

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“Analytics on the edge today can distinguish a cat from a person, a child from an adult, or someone wearing a hat - all without needing to send that data to a backend,” explained Joe James, Sr. Solution Manager at Hanwha Vision America.

This capability supports automated wrong-way detection, incident logging, and proactive enforcement, vital for improving road safety.

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Cloud can be a place for centralizing the data as it works very often right now or it can be also used for more complex or detailed recognition and system and device management. The hybrid model is where “the fun happens,” as Christian described. It allows continuous edge analysis while leveraging the cloud for tasks like model updates, multi-sensor correlation, and forensic searches across devices, critical for more complex use cases.

Geography also plays a role in technology adoption. The U.S. has embraced cloud AI faster than Europe due to more relaxed privacy regulations and greater comfort with third-party cloud services. In contrast, European cities continue to favor local servers and on-premise infrastructure to comply with stricter data protection laws. As Christian noted, “The ROI is not as strong in Europe as it is in North America, yet.”

Regardless of region, the direction is clear: cities are transitioning from collecting video to understanding it. The focus is shifting from installing more cameras to building smarter infrastructure - where data fusion, real-time insights, and scalable AI applications drive safer, more responsive cities.

Edge AI is not just a technological upgrade - it’s a strategic enabler for the future of public safety. And companies like FF Group, Genetec, and Hanwha are leading the way, not by delivering more data, but by delivering meaning.

To see how these ideas unfold in real-world discussions, watch the full summit session here: