With the introduction of video analytics, the automobile industry is taking on a new look. The latest AI and production combo promises a factory environment that is stronger, more efficient, and less stressful. With technology like video analytics, AI-powered production is going to alter how you operate, with less effort and better, more polished results. The basic purpose of AI-based Video Analytics is to automatically detect temporal and spatial events in videos.

Benefits of AI-based Video Analytics

It’s difficult to manage and maintain video surveillance systems, especially when there are a lot of them. Keeping track of everything that is going on is a pain, and it takes a lot of labor to do so. With video analytics, this is not the case. It analyzes video streams with comprehensive and advanced algorithms. Review camera images pixel by pixel to ensure that no information is lost. Analytics filters intelligently modify to meet specific security or business requirements.

What are the Challenges of Video Analytics?

  • For several years, the amount of data acquired by video analytics technologies has increased; data storage has become an issue as a result of the massive amount of data collected.
  • The information gathered by CCTV surveillance systems is as good as your team can handle. If your human resources are unable of effectively managing the knowledge you have deployed.
  • With more hacking and internet breaches being revealed every day around the world, the security component of your CCTV surveillance system becomes a crucial issue for your company’s daily operations.
With all of the problems listed above, consumers may find it difficult to choose a technology that meets their demands while also delivering positive results.

What are the technologies involved in Analytics of Video?

Video analytics is a difficult task; a video will be read frame by frame in video processing, and image processing will be performed on each frame to remove the features from that frame. There are numerous image processing libraries available. OpenCV is a free and open-source computer vision and machine learning framework that focuses on image recognition and video processing. Tensorflow, on the other hand, is a Google-developed open-source machine learning library for detecting high-precision objects. Video processing can be thought of as a combination of three fundamental tasks:

Object Detection

It is a type of computer vision that recognizes and locates items in an image or video. Using this identification and localization method, object recognition can count items in a scene and detect and record their exact positions, even while accurately labeling them.

Object Recognition

Object recognition is a type of computer vision that recognizes items in images or recordings. Object recognition is the major outcome of deep learning and machine learning algorithms. When people look at a photograph or watch a movie, we can swiftly recognize characters, things, situations, and visual information.Bring to the table win-win survival strategies to ensure proactive domination. At the end of the day, going forward, a new normal that has evolved from generation X is on the runway heading towards a streamlined cloud solution. User generated content in real-time will have multiple touchpoints for offshoring.Leverage agile frameworks to provide a robust synopsis for high level overviews. Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition. Organically grow the holistic world view of disruptive innovation via workplace diversity and empowerment.

Object Tracking

Object tracking is a computer vision discipline that aims to track things as they move through a sequence of video frames. Items in a soccer game are usually humans, but they can also be animals, cars, or other significant objects like the ball.

Real-Time Video Analytics

Surveillance teams are often unable to manually study the stored material in order to complete a post-incident report due to the enormous amount of data produced by video cameras.

Triggering Real-Time Alerts

Personal real-time warnings are activated when aberrant behavior is spotted, and video recognition technology improves situational awareness. Here are several examples:
  • Appearance similarity alerting: Video surveillance operators can design a warning based on entity appearance resemblance requirements.
  • Count-based alerting: Alerts can be issued when a specified number of objects (vehicles or persons) are spotted in a pre-defined place within a given time period.
  • If facial recognition technology is allowed, intelligence services may be able to utilize it to quickly identify offenders and issue warnings in real time based on digital images taken from film or externally supplied photographs. Learn more about Deep Learning Face Recognition and Detection.

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