by Mahesh Saptharishi, PhD,
Chief Scientist, VideoIQ, Inc.
As companies look more and more to active surveillance systems with video analytics, ease of installation and
maintenance becomes increasingly important. The term “calibration” is an integral part of any video analytics
installation. And while it is often discussed, it is typically not defined from the perspective of the end-user, and its impact on the initial installation and ongoing maintenance of the system rarely discussed — until now.
The generally accepted meaning of calibration in the video analytics market focuses on defining the height and size of a human in the specific field of view of an individual camera through a manual process. Vehicle detection, be it cars, boats or bikes, is calibrated in a similar manner, but for now we’ll focus exclusively on humans.
Traditional Calibration Process
Calibration is typically performed after the camera is mounted and multiple points in a scene are mapped out and recorded with a consistent object, such as a pole. The pole helps the camera determine the height of an average human being and trains it to trigger an alarm when something at that height enters a field of view. The expectation is that the field of view is not going to change dramatically, including landscape, trees and other objects, and the camera will never be repositioned or knocked during routine maintenance. The process is laborious, time-intensive and because it largely relies on a single characteristic, object height, manual calibration does not always provide the most optimal threat detection.