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Introduction to Video Analytics
Video Analytics enables a rapidly growing number of embedded video products such as smart cameras and intelligent digital video recorders with automated capabilities that would have required human monitoring just a few years ago. Broadly, it is the production of 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 business operations that required human interactions for data collection, training, etc. Are now being substituted with technological solutions. Transforming Data Into An Asset
It solutions build upon research incomputer vision, pattern analysis, and machine intelligence and span several industry segments, including surveillance analytics solutions, retail analytics solutions, 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 from it. 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 in deploying AI development services for video analytics and development in IoT devices and applications, channelizing data generation has become relatively straightforward through it. Artificial intelligence and deep learning technologies open better opportunities for businesses to drive value without direct programming. These models can be trained with a high video footage capacity to identify, classify, tag automatically, and label specific objects.
AI in Video Analytics
The automotive market is on its way to a new face with video analysis'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 and with less trouble and better, more refined results. Its 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 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 analysis. It uses comprehensive and complex algorithms to analyze recorded 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. Source - How AI Is Making An Impact On The Surveillance World
What are the Video Analytics Challenges?
- For many years, the amount of data collected from video analysis 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.
What are the technologies involved in Analytics?
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 key 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 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
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 mostly humans, but they may also be creatures, cars, or other 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
Via personal real-time warnings that are activated when unusual behavior is observed may need a response recognition technology increases situational awareness. Some cases include:
- Appearance Similarity Alerting
Based on entity appearance resemblance requirements, 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.
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
What are the Industrial Applications of Video Analytics?
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. 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.Retail
It can be used by brick and mortar stores 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
What are the Use-Cases of Video Analytics?
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, works by discovering and measuring facial features from a given image. It is a method for identifying an unauthorized /unknown person or verify a specific person's identity from their face. It's a computer vision branch, but face recognition is specialized and comes with social baggage for some applications. For face recognition, the algorithm notes specific essential measurements on the face like the color and size and slant of eyes, the gap between eyebrows, etc. All these put together define the looks encoding the information obtained from the image used to identify the particular face. And it allows tracking and managing authorized/unauthorized persons. Through that, we detect the person, whether it is permitted or not, and when the system sees the person is unknown, an alert will generate.
For Behavior Detection
Behavior detection is a method to detect the person's behavior towards the property of the building, organization, etc. here; the system will monitor each person's behavior and generate an alert if they seem to harm another person or infrastructure of the building. Through that, the workplace will detect a person in building how they behave in the workplace. They can see those people coming from outside for interviews or other purposes in the building. We can detect how their behavior in the workplace or harm to the place through our detection system. If any action is wrong, the system will alert the person and the building authority.
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. If the person has moved away from the edge, then that ID is dropped. If a new person appears, then they start with a unique ID. Another purpose of tracking people will be if the person is an outsider can in the workplace so by giving him the ID when he/she has arrived in the workplace so form that we can track the person in a building by his/her id, and you can see the person where and what they are doing.
For Crowd Detection
Crowd analysis/detection and scene understanding have drawn a lot of attention recently because it has a broad range of video surveillance applications. Besides surveillance, crowd scenes also 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 for a range of uses and applications to enhance situational awareness. Source: Moving From Surveillance To Intelligent Actions Using Digital Eyes
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 the information about the person at what time he/she has come 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 analyzing the presence and time management of the person in a building. A person has a period of his job, i.e., shift timing, so through which system has information about person's activity or record. When a person exceeds their allotted time, it sends alerts to the system 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 and however to be able to information, we need reliable ways of detecting fragments of object boundaries, a complex problem in itself. It will track the restricted zone entry, such as how many people are visiting the restricted areas daily, weekly or monthly, what is the time when they see the restricted/local area. The Boundary Box/Detection in the building will track the person in the restricted zone entry and through which you can analyze the person details who is going in the restricted area. The system will send the 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 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 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.Video Analytics Solutions
Many off-the-shelf applications in it, 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 this approach, which needs more optimized tools but the most organizations strive to obtain unique information to achieve individual objectives.Comparison
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.
Conclusion
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 analysis 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.
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