Those words are often associated with the real estate industry, but the security community can also apply that mantra to the all-important issue of camera placement for optimizing analytics.
One of the major knocks on analytics since its introduction has been that it doesn’t always deliver on expectations. End users want to gather all sorts of data ranging from license plate recognition to people counting to dwell and linger analysis, yet the results can be inconsistent.
The biggest culprit in creating patchy outcomes is improper camera selection and positioning. If this isn’t handled properly from the onset, the results from the analytics provided by these cameras will never meet expectations. No matter what else you do, if you mount a camera overhead and then decide you want it to read license plates, it can’t do the job. Similarly, a camera that is set up in the dead of winter near a parking lot surrounded by trees won’t be of much use when summer comes and the trees have grown and are now obscuring views, casting shadows and creating interfering movement.
Getting the most out of an analytics system has to begin long before the first images are captured. It starts with knowing what you want the analytics to do. This will then help determine the types and numbers of cameras used, the mounts on which they are installed, and the way they are positioned within various settings.
Fortunately, there are professionals within the manufacturer and integrator communities that can walk an end user through the process of selecting and installing cameras to maximize the analytics output. But, it is also important to know what the potential issues are for the different scenarios so you can make informed decisions.
There are many factors that impact video quality—illumination, the size of the asset, separating assets from people, obstructions, environmental issues and movement. We’ll take a close look at each of these and how proper camera positioning can work around them.