Video analytics at scale: Challenges and best practices in 2023

Artificial intelligence enables a rapid demand for embedded Video analytics 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 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 Video AI for Real-Time Insights

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.

Top Challenges in Video AI

• 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.

Technology Stack for Video AI

Analytics is a challenging job, a video analytics 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.