The development, implementation and operation of bespoke video surveillance systems has long been a central component of any effective commercial security installation, But as foundational technologies like cameras, storage solutions and processing systems have become more rampant and advanced, defensive capabilities have dramatically increased.
Among the most highly beneficial modern developments in the last couple of years has been the introduction of AI software and machine learning algorithms tech to allow stakeholders automate many essential surveillance processes and support human teams in new and exciting ways.
One notable area that you should never skimp on is video analytics. But how much do you know about this field? Well, video analytics is the process of observing and analyzing recorded video content to transform real-time information into intelligent and actionable insights.
In operation, this allows active video security systems to identify and observe various objects and stimuli associated with security incidents without assistance from human operators. For instance, video analytics systems can autonomously detect and observe vehicles, persons of interest, contraband items and unusual objects, warning staff of events that call for attention.
The decision to deploy video analytics security solutions removes the need for on-site personnel to manually observe CCTV feeds permanently. In their place, AI programs ensures sites are well-observed by warning security staff of events that require their attention and response.
Video analytics solutions work by counting on rule-based algorithms to discern whether stimuli in live surveillance footage represent a threat. Software tools will pose and answer a series of questions in an “if/then” decision tree. For example, if an object is detected and that object is the same size and shape as a weapon, an alert will be sent to staff requesting a response.
CCTV analytics systems observe live footage by isolating individual frames and inspecting the images in sequence. Rule-based algorithms continually analyze this footage in relation to the aforementioned decision tree, recording all changes in the form of intelligent metadata.
The recording and documentation of this data enable deep learning in video analytics, with this process helping to enhance threat detection capabilities. Over time, AI tools can learn patterns in data that then inform the operation of surveillance systems.
Video analytics cameras essentially understand movement, patterns and objects associated with normal and safe daily activity, bolstering the system’s ability to identify and address real security threats in almost no time.