Applied AI in Video Analytics

Applied AI in Video Analytics

As CCTV and more intelligent software have developed, video analytics technology has become more promising. Through video analytics, there are recognition of faces, number plates of the vehicles, counting of the objects, and detection of loitering, speed, and direction of the vehicles.


Applied Artificial Intelligence (AI)

Applied AI is a part of AI for the one who uses AI for completing real-life tasks. Video analytics is used to improve security and safety. Thanks to the AI algorithms for providing actionable intelligence in video analytics.


Video Analytics 

Analytics is a process to identify, interpret, and communicate meaningful data patterns. Business analytics apply this process for answering the business question, making the prediction, and making decisions. In short, analytics is a process of refining the raw findings and then applying analytical techniques for finding meaningful patterns. 


Video analytics is a technology that is used for processing video footage by using special machine learning (ML) algorithms for analyzing the footage for specified purposes. In the video, there will be automatic detection of the objects by using video analytics i.e. to identify the properties of the objects, recognize the people, actions of people, and events. For instance to recognize the specific faces to conclude if they are to be there at a particular time or not.  By using video analytics, the semantic analysis is also done i.e. surveillance by using activity detection.


Three common video analytics types are:

The fixed, and AL learning algorithms get the same results. They detect whether a suspicious behavior is happening or not in a scene. However, every algorithm takes different routes to achieve the result. The fixed algorithm analysis uses a pre-designed algorithm for a certain task, and to detect a certain behavior. The common behaviors that the fixed algorithm analytics detect are crossing the lines, moving in a wrong direction, leaving the articles, picking up the articles, and loitering.


Every fixed algorithm is designed to look for behavior. A client has to pay for every algorithm for every camera in many cases. AI learning algorithm operates differently. The algorithm starts as a blank slate. Then connecting to the given video camera for many days, the alerts are issued. During the period, the particular system learns what is normal during that day, weekdays, and weekends. After many weeks, the system begins to send alerts.  The third is facial recognition. This system can be used for identifying friends. This recognition system can also be used for investigations.


A typical facial recognition system matches points on the face with the samples that are stored in the database. If a face doesn’t match the recorded face then it will create a new record from the best available image of the person. 


The automotive market is on the way to the birth of video analytics. AI and production combinations promise a stronger, more efficient environment in the factories. AI transforms the work with the technologies i.e. video analytics to give better, and more refined results. The primary goal of applied AI video analytics is to detect the spatial events in the videos automatically.


How Video Analytics Is Delivered

Many cameras that are available in the market use ‚Äúedge analytics‚ÄĚ which means that the data at the sensors, network switches, or other devices are used instead of sending data back to the central data store. How Video Analytics Is DeliveredSome experts believe that these cameras are the best-suited cameras for indoor surveillance. For outdoor i.e. weather, lighting, insects, etc. are best handled by the server-based i.e. detection analytics. The server-based architecture receives the updates, and upgrades quickly and deliver more computational power. Moreover, the server-based architectures deliver more computational power that enables the use of the most advanced analytics algorithms that are based on AI.


Video Analytics Process

Video Analytics Process

  • Detection of Object: Object detection¬†in real-time in the video feeds has become possible by the algorithms i.e.¬†Mask R-CNN¬†and¬†YOLO. These algorithms detect the differences between the objects in a scene. For instance, these algorithms allow the video analyzing the program to detect and¬†also track the objects i.e. people, vehicles, lights, etc. in real-time. The detected objects are labeled and this labeling can be used for the tasks i.e. counting vehicles and people in crowded areas.


  • Motion Detection: Motion detection is a method of defining the activity in a specific scene. This detection is done by differences analysis in the image series. The detection of motion is generally carried out by the processes i.e. frame referencing, and pixel matching.


Benefits of Video Analytics

The video analytics technology is not only used for security concerns but also in the management of traffic, crowd, footfall analysis, social distancing, and detection of face masks. There is no doubt that video analytics is an attractive choice for many sites. This is useful in preventing the incidents that are related to property, and assets damages, theft, and disruption to the general business continuity. Video analytics technology can be connected to many other systems i.e. control of lighting, access management, and activating them when required.


Benefits of Applied AI in Video Analytics

It is a stretching task to monitor, and maintain video surveillance. It is difficult to track all that is happening, and a lot of manpower to tackle the happening. Applied AI video analytics uses comprehensive algorithms for analyzing the video streams. There are reviews of camera images pixel to pixel and no data is lost. 



Video analytics technology is helpful in our daily life. There are many sectors that can be benefited from this technology. Video analytics is playing its role in smart cities, and security control in hospitals and airports. Video analytics makes the processes more effective, and less tedious for humans, and companies.

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