Introduction to AI-based Video Analytics
The automotive market is on its way to a new face with video analytics’s birth. The latest AI and production combination promise a stronger, more efficient, and hassle-free working environment in factories. AI-powered production is set to transform how you work with technologies such as video analytics and with less trouble and better, more refined results. AI-based Video Analytics’s primary goal is to detect temporal and spatial events in videos automatically.
Benefits of AI-based Video Analytics
It’s challenging to monitor and maintain video surveillance systems, particularly when dealing with many cameras. It is a hassle to keep track of all that is going on, and it takes a lot of manpower to tackle it.
This is not the case with video analytics. It uses comprehensive and complex algorithms to analyze video streams. Reviewe camera images pixel by pixel, with almost nothing lost. Intelligently tailor to satisfy particular security or business requirements, analytics filters.
According to the Artificial Intelligence Global Surveillance index, at least 75 out of 176 countries globally are actively using AI-based surveillance technologies.
What are the Challenges of Video Analytics?
- For many years, the amount of data collected from video analytics tools has risen; data storage is becoming a problem with the tremendous volume of data obtained.
- The data obtained by CCTV monitoring systems are just as successful as your team can handle. If the human resources do not adequately handle the knowledge you have deployed to do so.
- With rising cases of hacking and internet breaches reported worldwide every day, the security component of the CCTV surveillance system raises a major problem for your company’s everyday operations.
With all the challenges mentioned above, it becomes challenging for consumers to select a technology that best fits their needs and simultaneously delivers successful outcomes.
What are the technologies involved in Analytics of Video?
Analytics of video is a challenging job, a video will be read frame by frame in video processing, and for each frame, image processing will be performed to remove the features from that frame.
There are many libraries for image processing. OpenCV is an open-source computer vision and Machine Learning library built primarily for Image Recognition and video processing tasks. On the other hand, Tensorflow is an open-source machine learning library created by Google to detect high precision objects.
It is possible to consider video processing as a mixture of three key tasks:
It is a form of computer vision that recognizes objects in an image or video and finds them. Object recognition can count items in a scene using this identification and localization method and determine and register their exact positions, even while correctly marking them.
Recognition of objects is a form of computer vision for recognizing objects in pictures or recordings. The main consequence of deep learning and machine learning algorithms is object recognition. We can quickly spot characters, things, scenes, and visual information while humans look at an image or watch a film.
Object Tracking is a discipline that seeks to track objects as they travel through a sequence of video frames in computer vision. In a soccer game, items are mostly humans, but they may also be creatures, cars, or other objects of significance, such as the ball.
Real-Time Video Analytics
Video cameras produce overwhelming amounts of data, so surveillance teams sometimes can’t manually review the stored footage to perform a post-incident report.
Triggering Real-Time Alerts
Via personal real-time warnings that are activated when unusual behavior is observed may need a response, and video recognition technology increases situational awareness. Some cases include:
- Appearance similarity alerting: Based on entity appearance resemblance requirements, video surveillance operators may customize a warning.
- Count-based alerting: Alerts can be activated when, within a given period, a certain number of objects (vehicles or people) are observed in a pre-defined location.
- Face recognition alerting: Intelligence services may use it to quickly identify offenders and issue warnings in real-time, based on digital images extracted from film or externally imported if facial recognition technology is approved.
Read more about Face Recognition and Detection with Deep Learning.
What are the Industrial Applications of Video Analytics?
There are various Industrial applications of AI-based Video Analytics. Listed below are several applications:
Solutions for video analytics may identify that a patient has not been examined according to their needs and warn the employees. In recognizing how to minimize wait times while providing direct entry to the emergency area, patient and guest flow measurement can be highly useful.
Smart cities / Transportation
In transport, video analytics has proved to be a huge support, helping to build smart cities.
Without appropriate traffic control steps, traffic especially in urban areas, can increase accidents and traffic jams.
Video Analytics can be used by brick and mortar stores to consider who their clients are and how they act.
Using Facial and license plate recognition systems in real-time to recognize persons and vehicles and make reasonable decisions.
How does Video Analytics work?
The design of a solution can differ depending on the individual use case but the scheme stays the same, so there are two different methods of reviewing video content: in real-time, by configuring the device to trigger warnings for particular events and accidents that occur at the moment, or in post-processing, by running specialized searches to enable forensic investigation activities.
Video Analytics Solutions
Many off-the-shelf applications in AI-based Video Analytics, from classic surveillance platforms to more complex situations such as smart homes or healthcare software. If one of these standard solutions satisfies your use case, they could be an alternative for you. Generally perform some form of program adaptation or parameterization, and these implementations only allow customization to a certain extent.
However, with a video analytics approach, which needs more optimized tools but the most organizations strive to obtain unique information to achieve individual objectives.
However, tracking each camera continuously is nearly impossible for security personnel. So this means employees do not even have extensive situational awareness. In comparison, surveillance cameras capture overwhelming amounts of videos and still, if they need to perform a post-incident report, security personnel also don’t have the resources to manually review the stored footage.
Read More about Smart Attendance System with Face Detection
In our everyday activities, AI-based Video Analytics solutions help us. Many sectors can benefit from this technology, especially as the sophistication of possible applications in recent years has increased. The area of video analytics makes both more reliable and less repetitive processes. It is less costly for enterprises, from smart cities to surveillance controls in hospitals and airports. To individuals tracking retail and shopping centers. We recommend talking to our expert.