Most security systems are equipped with CCTV cameras to helps security teams identify potential threats. But the larger and more complicated an installation is, the more difficult it can be to ensure all active cameras and surveillance feeds are observed appropriately.
Thanks to the advent of intelligent techs like AI and Machine Learning, modern surveillance systems can be programmed to identify anomalous events and security stimuli automatically, helping teams focus their efforts on unfolding events and matters of immediate importance.
This is the basic premise of video analytics. After all, it can independently analyze and draw insights from video content to bolster decision-making and enhance the performance of security responses.
Video analytics surveillance systems allow for a more practical and effective way to review and observe security footage. Content captured by multiple cameras over a span of several days can be automatically sorted by matters of interest, supporting security personnel in identifying and appropriately responding to suspicious activities in real-time and during investigations.
So, how does video analytics work in delivering remarkable results? Well, video analytics systems process video feeds using algorithms designed to detect specific stimuli. Captured images are reviewed in sequence by dedicated software tools that are programmed to check for certain events or objects that could signify a security threat.
In simple explanation, video analytics searches for anomalous differences in a sequence of images and then generates insights into these events using rule-based algorithms. For instance, if a video camera captures an object moving through its field of view, video analytics will ask questions to help define the object and decide if its presence calls for further action.
The thing with video analytics cameras is that they ensure key areas are always observed, with different video analytics algorithms precisely designed to search for specific stimuli. Among the most common types of analytics include Automatic License Plate Recognition (ALPR), crows’ detection facial recognition, people counting, object tracking, and motion detection, to mention a few.
Bespoke types and combinations of video analytics tools can be developed to meet different use cases across different industries. Business owners, professionals, and security teams may use off-the-shelf to address common security and organizational management needs or invest in creating custom solutions to meet unique industry requirements.
Real-time video analytics provide numerous significant benefits to businesses across most major sectors, enabling professionals to gain actionable insights into important security, infrastructural and organizational processes.
