The hidden costs of video security analytics

Among these professionals, there is a general awareness that the software and video cameras needed for analytics can be expensive. These costs are often highlighted within articles that appear in related search results. But in this article, we’ll talk about the lesser-known (hidden) costs of analytics. 

If you work in security and/or IT, you might’ve already come across some or all of the items on our list. Please read on; we’re eager to know if your experience matches ours.

Security cameras have come a long way. Some of them have basic built-in analytics. One example is line crossing, where an alert goes out when a moving object crosses a user-defined line (e.g., someone jumping over a fence). But even with basic analytics, there’s no such thing as “plug and play”. What vendors sometimes fail to mention is that:

1. Even if your cameras can handle the analytics, someone will need to log into each camera interface at a time to get it set up. Unless you want to pay for a separate tool that lets you deploy everything in bulk. 

2. Once the analytics have been running for a little while, you’re going to need to go back and fine-tune each camera’s settings, placement, angles and detection range. 

If you’re a seasoned veteran in the world of security, these parts of the job will likely be familiar. But they can still be easy to overlook when drafting a realistic budget.

Now let’s consider examples of more heavy-duty analytics. Occupancy monitoring needs to combine data from several cameras. AI license plate recognition and facial recognition rely on databases. Heat mapping is dependent on historical data. All of these need to run on a server rather than on a camera. But how expensive can this all get? That depends.

1. The bulk of your server room costs will come from storage requirements, so that’s an important budget line to highlight. 

2. While video management software (VMS) solutions such as our own Milestone XProtect can perform analytics such as forensic search on the server side of things, organizations use separate tools for analytics for, say, facial recognition. These tools have specific processor and RAM requirements, so it’s worth shopping around. 

3. Another way to cut down costs is to consider what proportion of your site actually needs the heavy-duty stuff. Let’s say you’re looking at a project in an airport. You might want facial recognition software coverage across all spaces in all terminals. This would let you search for a known face among the crowds and get a recorded video of the person of interest presented as a timeline. But it would also cost quite a lot. Some airports decide to keep costs down by only having facial recognition in the passport check/immigration area. They might run analytics on supercomputers for 100 cameras dedicated to this priority area. Meanwhile, their remaining 1,900 security cameras around the airport might rely on less resource-intensive analytics on lower-budget servers.

While the promise of machine learning is enticing, there is a conspicuous absence of people asking who will teach the algorithms. Let’s say a manufacturing company wants to implement personal protective equipment (PPE) detection. In other words, they want their cameras and deep-learning algorithms to work together to automatically identify and notify them when someone isn’t wearing the mandatory helmet, safety boots, gloves, etc. Sounds good. Safety first. 

But software isn’t always good at telling the difference between a hat and hair, or differentiating between normal clothing and protective gear if the colors and shapes are similar. The initial functionality depends a lot on how much time and care a real live person (or people) spent 1) installing cameras for optimal coverage and visibility, and 2) training the algorithm on examples of proper PPE versus PPE violations. Regardless, false alarms will happen. And mistakes will be found and reported by humans; a machine won't know about its mistake until feedback is manually entered. 

You’re going to need to pay for calibration services. In other words, someone who can analyze the data, identify the reason(s) behind false detections and mitigate the underlying issues. This usually consists of a minimum of two visits; one for analysis and initial implementation of improvements, and one for comparing the results and reprocessing. Whether these “teaching” services are hired in-house or outsourced, a cost is a cost.

The VMS you use can also have an impact on the cost of analytics. 

Here’s why customers choose Milestone XProtect:

1. XProtect is very good at making the most of metadata. The Search function lets operators quickly find evidence of specific events, objects and/or people. This type of analysis takes place on the server side, as it relies on reviewing video from multiple cameras. But XProtect’s Search is very efficient, so these costs are quite low. 

2. When it comes to working with third-party analytics like facial recognition, the hardware requirements of XProtect are almost always lower than alternative options. Usually by a significant factor.

3. Some VMS vendors charge extra for integrating with third-party analytics. But Milestone doesn’t charge extra for integrating with XProtect. 

4. Because it’s an open-platform VMS, XProtect lets you combine analytics from different camera brands at no additional cost. Open platforms are also useful for server-side analytics. You’re not locked into any particular analytics tool, as XProtect works with pretty much everything. 

5. You have the flexibility to start off with camera-side analytics and potentially wait until the next budget period to invest in third-party server-side analytics. This also gives you more time to decide how many cameras will need the more expensive analytics.

So, what do you think of these costs? Did we forget to mention anything? If you’d like to share your experience and/or if you’d like to see XProtect in action, please send us a message or book a demo.