Video Analytics are sets of processes that use AI algorithms and metadata to scan video in order to detect patterns or recognize specific objects. Once processed, the video data can then be queried and searched for various/specific results. These are also known as IVAs - Intelligent Video Analytics.
Gorilla Technology’s IVAR (Intelligent Video Analytics Recorder) provides real-time video analytics, monitoring, and video IoT platform management.
The global video analytics market has a projected 21.3% CAGR to 2027.
Gorilla is leading the market by pairing our video analytic specialization in over 40 IVAs with leading IPCs from partners like Lanner. Considering the digital transformations COVID-19 has spurred many to undertake, the delivery and quick deployment of cutting edge AI Appliances focused on physical, data, and network security has proven invaluable.
Learn more about video analytics market trends here.
The processes involved in getting IVA output from software is similar to how people visually detect and recognize things. The essence of what video analytics does is generally described in three steps.
Depending on the goal, video needs to be processed using different methods in order to deliver relevant results. Gorilla has categorized the most widely used types of analytics into five fundamental IVA groups which are described in more detail below.
These analytics use algorithms that are designed to look for a specific behavior.
Thinking more deeply, a behavior could be defined as action over time. With that in mind, each Behavior Analytic needs more than one frame from the video to determine if an event or behavior has occurred. So it follows that the algorithms in Behavior Analytics look for changes from frame to frame over time to identify a very specific and predefined event or action. We’ve broken down and classified the Behavior Analytics that are used in our solutions here:
The People Counting IVA does just that, it detects and counts people for a specified amount of time as they enter a zone and/or cross a line which users define in the software.
This IVA detects when people cross a line (or lines) of user defined length and position.
Intrusion Detection monitors user created zones to detect any activity or entries by moving objects (like people).
This IVA monitors a user created zone for people moving A) within the zone AND B) in the marked direction. Movements in the opposite direction do not trigger an alert.
Same as the direction detection IVA but detects and alerts to movements in the opposite direction. As an example, security checks at airports and other transportation hubs stand to benefit from this type of IVA.
The Loitering Detection IVA monitors figures or people entering and then remaining in a user created zone for a specified period.
People and Face Recognition could easily be sliced into two core groups, but we keep them as one since they are so closely related. As Behavior Analytics need to detect human shapes to perform effectively, People/Face Recognition IVAs are next.
The Human Detection IVA detects human figures within the video. Once detected, features like clothing color, gender, eyewear, masks, and age group can be detected as well.
This IVA recognizes and identifies faces. This is used in conjunction with Gorilla’s BAP software and its facial recognition database. While uses for this are myriad (and often in the news), we most often see Face Recognition used for Watch Lists, VIP identification, Attendance Systems, and Black Lists.
AI has been widely deployed by transportation authorities to keep traffic flow running smoothly, reduce traffic violations and aid with crime investigations. Edge computing generates real-time events and statistical data which can be used for timely decision-making and less workforce deployment.
Detects vehicle types, e.g. motorbikes, cars and buses
Counts vehicles that move in a specific direction
Recognizes the vehicles that violate traffic regulations or enter into prohibited areas
Recognizes license plates on static or moving vehicles
Replace the Face Recognition IVA with any given object and you’ll get the Object Detection IVA. This is where algorithms are used in training the software to detect and recognize a specific object, like a hot dog. There are a lot of different objects in the world, so the training and size requirements add up quickly.
Dashboards in software showing data about various business activities are a valuable asset in just about any retail or enterprise setting. Using video analytics from within a dashboard to enrich and increase results should be a part in everyone’s toolbox.
While the IVAs in numbers one through four above are widely used for surveillance scenarios, there are magnitudes of business scenarios that can reap the benefits that video analytics offers. To see some examples, check out how Gorilla is applying these to create intelligent solutions for multiple business markets and industries.
As you read above, these IVAs all orchestrate various algorithms to achieve and deliver results. Essentially though, IVAs detect for and determine if a defined event or behavior has been found or occurs within a video camera’s field of view and then notifies the designated user of the finding.
In a similar manner, most of us go through varying processes depending on if we’re searching for keys at home or for our friend in a busy station.
Thinking about the entire process, could there be a single solution that can do everything effectively? It seems like an insurmountable amount of tasks: from processing each single frame’s analytic data to displaying it together with the video, into creating a complete video system with an array of user selectable & customizable IVA in a building or any other scenario, all the way to putting multiple systems together that report back to a central control center.
It’s not impossible. To demonstrate this, let’s look at what IVAR® from Gorilla can do and how it operates.
Video analytics as a whole requires a lot of dedicated processing power. We should keep in mind here that before optimization and edge devices with capable CPUs, video analytics was processing both video and analytic data on one machine and required additional GPUs to do most of the work. Technology and the ability to split these two up has advanced to the point that it’s now possible to keep the video data at the edge while pushing the analytic data up the network for quick processing.
One technology, which Gorilla was the first to adopt, is the Intel® distribution of the OpenVINO™ toolkit. Using the OpenVINO™ toolkit to optimize IVAR keeps deployment and upkeep costs low while decreasing operating temperatures by minimizing the need for expensive GPUs.
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