Video analytics scan incoming video feeds to (1) optimize storage or (2) to identify threatening/interesting events.

Storage optimization is the most commonly used application of video analytics. In its simplest form, video analytics examines video feeds to identify changes in motion. Based on the presence or absence of motion, the video management system can decide not to store video or store video at a lower frame rate or resolution. Because surveillance video captures long periods of inactivity (like hallways and staircases, buildings when they are closed, etc.), using motion analytics can reduce storage consumption by 60% - 80% relative to continuously recording.

Using video analytics to identify threatening/interesting events is the more 'exciting' form of video analytics. Indeed, generally when industry people talk of video analytics, this is their intended reference. Common examples of this are perimeter violation, abandoned object, people counting and license plate recognition. The goal of these types of video analytics is to pro-actively identify security incidents and to stop them in progress (e.g., perimeter violation spots a thief jumping your fence so that you can stop them in real time, license plate recognition identifies a vehicle belonging to a wanted criminal so you can apprehend him).

These video analytics have been generally viewed as a disappointment. While many observers believe that video analytics will improve, the video analytics market is currently contracting (in response to its issues and the recession). EpiCamera R & D Department is working on video analytics to be used in our system and will let our precious users know about it once they are ready to be launched.