Adequate video surveillance coverage depends entirely on a facility's layout, square footage, and security goals. Historically, better coverage required more cameras, but technology innovations are changing those expectations and undermining previously held rules of thumb. We asked our Expert Panel Roundtable: Do today's video systems generally require more cameras or fewer cameras, and why?
Generally, we are still seeing a steady, sustained growth in the number of cameras and other edge sensors globally – but the growth drivers vary for each application. Across many industries, the focus has shifted from maximising camera counts to selecting the right combination of technologies for each environment. An airport, manufacturing facility, or school will likely require a different mix of devices based on its operational and security objectives. Today's video systems are typically part of a broader ecosystem of connected devices, where cameras work alongside radar, LiDAR, access control, audio, and other sensors to give operators better context and situational awareness. At the same time, cameras themselves have become more capable. Higher resolutions, multi-sensor designs, and built-in analytics often allow organisations to cover areas that previously required multiple devices. This is where open architecture becomes increasingly valuable. Organisations want the flexibility to combine technologies from different manufacturers and manage them in a single unified system. That gives them the freedom to adopt new capabilities as requirements evolve, rather than being tied to a single vendor's ecosystem.
For most deployments, the answer is fewer cameras. Advances in sensor technology mean a single device can now cover areas that previously required multiple cameras. Higher resolutions preserve image detail across wider fields of view, while multi-sensor cameras with two to five imaging sensors provide comprehensive coverage from a single mounting location. That reduces installation costs, simplifies infrastructure, and minimises maintenance without sacrificing situational awareness. Artificial intelligence (AI) analytics have also increased the value of every camera by turning them into intelligent edge devices that classify events, generate metadata, and deliver actionable insights rather than simply recording video. The result is that organisations can often achieve better outcomes with fewer, more capable devices. That said, every deployment should be designed around operational requirements rather than camera counts alone. The objective is not to install the most cameras, but to provide the right coverage, intelligence, and resilience for the environment while maximising long-term return on investment.
I will push back a little on the premise here. The trend is less about the absolute number of cameras and more about the volume and variety of data streams those cameras now generate, which is not restricted to video streams. Camera count used to be the default way people measured system growth, but that thinking has not kept pace with what today's hardware can do. A modern camera is a smart edge device, where a single unit can comprise multiple image sensors, together with onboard analytics, metadata and event generation, audio capture, etc., such that each may be producing far more usable data streams than its predecessors ever did, often with substantial onboard processing that used to require a dedicated server down the hall. What changes, especially in a heterogeneous vendor environment is less about capacity and more about coherence; making sure video, audio, and sensor data, processed through increasingly sophisticated AI, all come together into something operators can actually work with instead of a fragmented muddle. That's where a video management system developed with an open platform philosophy earns its keep, pulling all of data into one operational whole.
Today's systems achieve the same or greater coverage with fewer physical devices than they did a decade ago. A single multi-sensor or panoramic camera now covers areas that once took three or four separate units, cutting installation, cabling, and maintenance costs across a deployment. But fewer devices does not mean less video. Each unit holds several lenses, so coverage and detail go up even as the camera count comes down. The net result is surveillance that is both more capable and more cost-efficient than it was a decade ago. For integrators, this shifts the core planning question. The issue is no longer how many cameras a site needs, but how much resolution, bandwidth, and storage the system behind them can handle, and how well it turns that into usable detail, whether for live operators or analytics flagging events automatically. Increasingly it is the video management system (VMS), not the camera count, that defines what a deployment can do.
In my experience, organisations are worried less about camera count and more about whether they have the right cameras in the right locations. Thanks to significant advances in imaging technology, edge processing, and AI-powered analytics, individual cameras deliver far broader coverage and greater operational value compared to a decade ago. Meanwhile, organisations are expanding surveillance beyond traditional security applications to support safety, operations, compliance, and business intelligence, creating demand for additional coverage in areas that were previously unmonitored. As a result, we are seeing two trends happen simultaneously: organisations are becoming more strategic about camera placement while also increasing the overall value they expect from their video systems. The outcome may require more cameras in some environments and fewer in others; however, across applications, success is measured by visibility, actionable insights, and ease of management — not camera count alone.
Current and emerging video systems may require either fewer or more cameras depending on their purpose. For basic security and situational awareness, high-resolution, panoramic, multi-sensor, and plan, tilt and zoom (PTZ) cameras can replace multiple legacy devices by covering larger areas with fewer installations. Conversely, organisations using AI-driven analytics, operational monitoring, or computer vision often require more cameras to capture precise, uninterrupted views and generate high-quality data for AI analysis and training. As video systems migrate to IP-based networks and cloud-based Video Surveillance as a Service (VSaaS) platforms, they create cybersecurity and legal risks. Cybersecurity risks include compromised IoT devices, unpatched vulnerabilities, unsecured video transmission, and supply chain threats. Compliance obligations continue to expand under evolving privacy, biometric, AI, and cross-border data protection laws, with substantial penalties for non-compliance.