There are few discussions in the physical security business that don’t at some point focus on the topic of cybersecurity. One area frequently missing from these conversations is the importance of a trusted supply chain for manufacturers. Since a product is only as good as the hardware and software inside it, examining how something is built can give us rapid insight into its potential vulnerabilities and overall cyber worthiness. The NDAA (National Defense Authorization Act) ban is particularly focused on the subject of component sourcing for security devices. What is inside that device that could be exploited? Where did it come from? What do we know about the manufacturing process? These are all important questions about the manufacturing supply chain that need to be considered by anyone who cares about cybersecurity.
Artificial intelligence (AI) presents a perfect solution to compensate for unmanned environments or those with limited staffing, or the loss of vigilance after looking at a screen too long. AI can help us not only watch continuously, but also feed systems that are able to sort, organize and categorize massive amounts of data in a way that human operators cannot. And it can do so far more reliably than traditional video analytics ever did.
In the world of video cameras, it’s well understood that higher megapixel (MP) image sensors in a camera can capture more picture detail. However, there’s much more to image quality than pure megapixels since the quality and size of the sensor along with the lens plays a crucial part in determining the quality of each pixel.
When budgeting for video surveillance cameras, there are multiple factors to consider that affect cost beyond the camera itself. It’s important to also know the cost of installation, and the cost to service or upgrade a unit in the future. While configuring a small number of cameras will likely have little impact on cost, the labor involved in installing and servicing hundreds of cameras can be significant. A modular approach to camera design is necessary to reduce the installation costs and long-term maintenance of such cameras.
It’s helpful to reflect on where we are now versus where we are going. Today, there is still more discussion about what might be possible than actual physical products on the market. Much of the conversation centers on practical ways to utilize deep learning and neural networks and how these techniques can improve analytics and significantly reduce false-positives for important events.
While the concept of the multi-sensor or multi-directional camera is not new, there have been noteworthy advancements that make these cameras the best choice for many types of security installations. In the past, many multi-sensor cameras were not able to deliver high frames per second / per sensor (fps) for smooth, clear motion capture and frequently represented a compromise in performance.
Small and mid-sized businesses (SMBs) face unique challenges when choosing a security solution. While large businesses enjoy entire departments devoted to addressing the many facets of security – video surveillance cameras, video management, access control, network infrastructure – SMBs have limited resources to help them select and maintain a security solution.