Introduction to Video Analytics with Generative AI
Artificial intelligence enables a rapid demand for embedded video analytics surveillance, such as smart cameras and intelligent digital video recorders with edge AI automated capabilities that would have required human monitoring just a few years ago.
Generative AI provides the capabilities to search digital video content at a particular period, sentimental analysis, and tone analysis. Broadly, it produces meaningful and relevant information from digital video. As opposed to video compression, which attempts to exploit the redundancy in digital video to reduce the size, analytics is concerned with understanding the video's content.
The video analytics industry is projected to reach $50.7 billion by 2032, highlighting the growing demand for advanced video analysis solutions.
The Generative AI industry is anticipated to hit $118.06 billion by 2032, showcasing its transformative potential in automation and content generation.
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Video Management Software Systems and Video Motion Detection solutions build upon research in computer vision, detection analytics technologies, motion detection, pattern analysis, and machine intelligence and span several industry segments, including surveillance, retail, and transportation analytics solutions. It is also called video content analysis (VCA) or intelligent video.
Artificial intelligence (AI) and machine learning (ML) are emerging as launch intelligence factors to generate value. AI for video analytics is breaking through builds units, supply chains, workplaces, and retail safety value to automate and support analytical operations.
With large-scale experience deploying real-time and intelligent video analytics solutions for CCTV monitoring Operators and EDGE AI development in IoT devices and applications, channelizing data generation has become relatively straightforward. Generative AI and Deep learning models can be trained with a high video footage capacity to identify, classify, tag automatically, and label specific objects.
How Generative AI Enables the Video Surveillance Market
The automotive market is coming to a new face with the birth of video analysis. The latest AI and production combination promises a more robust, efficient, and hassle-free factory working environment. AI-powered production is set to transform how you work with technologies with less trouble and better, more refined results. Its primary goal is to detect temporal and spatial events in videos automatically.
Benefits of Video AI for Real-Time Insights
Monitoring and maintaining surveillance systems is challenging, mainly when dealing with many cameras. It is a hassle to keep track of everything happening, and it takes a lot of manpower to tackle it. This is not the case with analysis. It uses comprehensive and complex algorithms to analyze recorded streams. Review camera images pixel by pixel, with almost nothing lost. Intelligently tailored 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. Source - How AI Is Making An Impact On The Surveillance World
Overcoming Challenges in Data Management and Security
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For many years, the amount of data collected from video analysis tools has risen; with this tremendous volume of data, data storage is becoming a problem.
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The data obtained by CCTV monitoring systems are just as successful as your team can handle. If the human resources do not adequately address the knowledge you have deployed to do so.
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With rising hacking and internet breaches reported worldwide every day, the security component of the CCTV surveillance system raises a significant 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.
Technology Stack for Real-Time Video Analytics with Generative AI
Analytics is a challenging job; a video will be read frame by frame in 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 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 processing as a mixture of three essential tasks:
Object Detection
It is a form of computer vision that recognizes objects in an image 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.
Object Recognition
Recognition of objects is a form of computer vision that recognizes 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
Object Tracking is a discipline that seeks to track objects as they travel through a sequence of frames in computer vision. In a soccer game, items are primarily humans, but they may also be creatures, cars, or objects of significance, such as the ball.
Real-Time Video Analytics
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
Personal real-time warnings activated when unusual behaviour is observed may need a response recognition technology to increase situational awareness. Some cases include:
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Appearance Similarity Alerting - Surveillance operators may customise a warning based on entity appearance resemblance requirements.
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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.
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Face Recognition Alerting - If facial recognition technology is approved, Intelligence services may use it to identify offenders quickly and issue real-time warnings based on digital images extracted from film or externally imported.
Face Recognition uses computer algorithms to find specific details about a person's face. Click to explore about our, Face Recognition and Detection with Deep Learning
Top Industrial Applications for Intelligent Video Analytics Solutions
There are various Industrial applications of AI-based Video Analysis. Listed below are several applications:
Healthcare
Solutions for video analytics may identify that a patient has not been examined according to their needs and warn the employees. Patient and guest flow measurements can be instrumental in recognizing how to minimize wait times while providing direct entry to the emergency area.
Smart Cities / Transportation
In transport, video analytics has proved to be a tremendous support, helping to build smart cities. Traffic can increase accidents and jams without appropriate traffic control steps, especially in urban areas.
Retail
Brick-and-mortar stores can use it to consider who their clients are and how they act.
Security
Using Facial and license plate recognition systems in real-time to recognize persons and vehicles and make reasonable decisions.
Text-Image Analytics Solutions for Enabling Enterprises to derive actionable insights from Images to increase business efficiency. Source: Computer Vision Services and Solutions
Top Use Cases of Real-Time Video Analytics with Generative AI
Described below are the brief use cases of video analytics in the workplace.
For Face Recognition
A facial recognition system is a technology-efficient parallel to a human face from a digital image or a video proportion against a database of faces, typically employed to verify users through ID verification services, which works by discovering and measuring facial features from a given image. It is a method for identifying an unauthorized /unknown person or verifying a specific person's identity from their face. It's a computer vision branch, but face recognition is specialized and has social baggage for some applications.
For face recognition, the algorithm notes essential measurements on the face, like the colour, size, and slant of eyes, the gap between eyebrows, etc. All these, together, define the looks encoding the information obtained from the image used to identify the particular face. It allows tracking and managing authorized/unauthorized persons. Through that, we detect whether the person is permitted, and when the system sees the person is unknown, an alert will be generated.
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For Behavior Detection
Behaviour detection is a method to detect the person's behaviour towards the property of the building, organization, etc. Here, the system will monitor each person's behaviour and generate an alert if they seem to harm another person or the infrastructure of the building. Through that, the workplace will detect a person in the facility and how they behave. They can see those people coming from outside for interviews or other purposes in the building. We can detect their behaviour in the workplace or harm to the place through our detection system. The system will alert the person and the building authority if any action is wrong.
For Person Tracking
Person Tracking is an essential domain in computer vision. It involves the process of tracking a person across a series of frames. We would start with all possible detections in a frame and give them an ID for people tracking. In subsequent edges, we try to carry forward a person's ID. That ID is dropped if the person has moved away from the edge. If a new person appears, then they start with a unique ID. Another purpose of tracking people will be to determine if the person is an outsider in the workplace by giving him or their ID when he/she has arrived so that we can track the person in a building by his/her ID. You can see the person where and what they are doing.
For Crowd Detection
Crowd analysis/detection and scene understanding have drawn much attention recently because it has a broad range of video surveillance applications. Besides surveillance, crowd scenes exist in movies, TV shows, personal video collections, and even videos shared through social media. Since crowd scenes have many people accumulated with frequent and heavy closure, many existing detection, tracking, and activity recognition technologies, which are only applicable to sparse settings, do not work well in crowded locations.
Companies can use surveillance technology with internet-enabled data collection and cloud-based ML processing to enhance situational awareness for various uses and applications. Combines technologies like the Internet of Things (IoT), Artificial Intelligence
People Count / People Presence
For People Presence or People Count with the system, you will analyze the person's attendance and track the people in the building. To get information about the person and when he/she came to the building. You can also detect the number of persons in the building, and the workplace will get the details of each person in the building.
Time Management
It allows for the analysis of the presence and time management of the person in a building. A person has a period of his job, i.e., shift timing, through which the system has information about the person's activity or record. When a person exceeds their allotted time, it alerts the design and the respective person. It will also track the number of authorized/unauthorized persons exceeding their time in the building.
Zone Management and Analysis/Boundary Detection
The powerful visual for recognizing the object/person in images in the restricted area; however, to obtain information, we need reliable ways of detecting fragments of object boundaries, which is a complex problem. It will track the restricted zone entry, such as how many people visit the restricted areas daily, weekly, or monthly, and what time they see the restricted/local area.
The boundary box/Detector in the building will track the person in the confined zone entry, and through this, you can analyze the person going into the restricted area. The system will send alerts to the person who has gone to the local area on the system, and you can see the boundary box on the regions and the person.
How does Generative AI-Powered Video Analytics work?
The design of a solution can differ depending on the individual use case. Still, the scheme stays the same, so there are two different methods of reviewing recorded 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.
Intelligent Video Analytics Solutions
Many off-the-shelf applications exist, from classic surveillance platforms to more complex situations like smart homes or healthcare software. It could be an alternative if one of these standard solutions satisfies your use case. Generally, these solutions perform some form of program adaptation or parameterization, and these implementations only allow customization to a certain extent. However, with this approach, which needs more optimized tools, most organizations strive to obtain unique information to achieve individual objectives.
Generative AI for Real-Time Insights
However, tracking each camera continuously is nearly impossible for security personnel. This means employees do not even have extensive situational awareness. In comparison, surveillance cameras capture overwhelming amounts of videos. Still, if they need to perform a post-incident report, security personnel also don't have the resources to review the stored footage manually.
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