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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.

What are the Use-Cases for it?

Listed below are the Use-Cases.
  1. Face Recognition
  2. Behavior Detection
  3. Person Tracking
  4. Crowd Detection
  5. People Count / People Presence
  6. Time Management
  7. Zone Management and Analysis/Boundary Detection

Many sectors can benefit from AI-based Video Analytics, especially as the sophistication of possible applications in recent years has increased.Click to explore about our, AI-based Video Analytics

Use-Cases of video analytics in the workplace

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.

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Why Xenonstack?

To solve these problems, Xenonstack came with a Video Analytics solution to make workplaces productive and safe using AI. The system detects a person; based on appearance; it recognizes the visitors or regular employees. Then accordingly, monitor access management, behavior analysis, anomaly detection, and alert management to make the system more reliable. It analyzes crowd and presence using self-learning analytics. Besides, it monitors suspicious activities and sends an alert to the respective authorities for the actions if encountered. Thus the system will monitor the whole process using it. The solution will cover the following scenario to solve a problem:
  • Identification
    • Authorized
    •  Unauthorized
    • Temporary Access granted
  • Behaviour Analysis
    • Anomaly Detection(Live Tracking)
    • Prohibited Area
    • Access Control
  • Exit
    • Map Person

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Today, machines can automatically verify identity information for secure transactions, surveillance and security tasks, access control to buildings, etc. These applications usually work in controlled environments, and recognition algorithms can take advantage of the environmental constraints to obtain high recognition accuracy. However, next-generation face recognition systems will have widespread applications in intelligent environments where computers and machines are more like helpful assistants.

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