In the realm of research and development, image and video processing are the two hot topics. Video processing is a type of signal processing that uses video files or video streams as input and output signals.

Video footage is increasingly getting managed using artificial intelligence (AI). Deep learning-based computer vision algorithms are used to identify concepts and faces in video streams, classify movies, add subtitles automatically, and improve videos and images using techniques like super-resolution.

The data channel gets more complicated when dealing with real-time video processing. In addition, we are functioning to reduce streaming video latency. However, we must ensure that the implemented models are accurate enough.

Grand View Research reports that the video stream market will be worth USD 184.27 billion by 2027.

Video processing is a sequence of consequent processes like decoding, computation, and encoding. Working in parallel and optimizing the algorithm for speed are the two easiest ways to achieve accuracy.

In general, file splitting and pipeline design are the two approaches to parallelizing processes

File Splitting:

By splitting video files, the algorithms work simultaneously, leading to faster, more precise models. To do this, the videos are split into different parts and then processed simultaneously.
Video splitting is a form of virtual file generation, rather than sub-file generation.
Still, video file splitting isn't the best choice for real-time video processing. How so? You cannot pause, resume, or reverse a file during this process.

Pipeline Approach:

Instead of breaking the video, the pipeline technique aims to partition and parallelize the processing activities. The pipelining is more adaptable as a result of this approach.

How to create a live video processing system using Artificial Intelligence

Here is a brief note on how to create your live video processing system:

Adjusting a pre-trained neural network (or trained) to be able to perform the tasks needed.
Setting a cloud infrastructure to enable processing of the video and be scalable.
Building a software layer to pack the operation and implement user strategies (mobile applications, web and admin panels, etc.)

Uses of Neural Networks for Video Processing

With the help of deep learning algorithms and neural networks, machines can be prepared to see and analyze videos in the way required for a certain task.
Progress in the execution of AI algorithms for video processing is outstanding and opens a wide range of possibilities in fields from pharmaceutical and agribusiness to retail and law enforcement.

Why do you need to choose Intelgic?

Intelgic develops AI and deep learning solutions based on the latest study in image or video processing. We use frameworks such as Keras, TensorFlow, and PyTorch.
When working with machine learning projects and/or dealing with pictures/videos, we likely use convolutional neural networks.
But, before we use convolutional neural networks, we preprocess the frames and solve some other subtasks via different methods.

Use-cases of video processing

Tracking of individuals

It is an integral aspect of computer vision to track people. A person gets tracked over a sequence of frames.
We put all possible detections in a frame and give them a distinctive identifier.
The ID of a person is carried forward in the following edges. This ID gets declined if the individual moves away from the edge.
If a new person appears, they will have their unique ID.
Another reason for monitoring people is to check where and what they accomplish or an outsider in the company.

Detection of behavior

The system will monitor each individual's behavior and generate an alarm if they appear to hurt another person or the building's infrastructure.
Conduct detection is a method to detect the person's behavior towards the possessions of the building, organization, etc.
The workplace will witness a person's behavior in their workplace via this approach.
They can see individuals entering the building from the street for consultations or other reasons.
Through our detection technology, we can decide how a person's behavior at work impacts their surroundings.
The system will notify the user and the building authorities if any step is mistakenly taken.

Time Management

It enables the analysis of a person's presence and time management in a system.
A person has a term of occupation, i.e., shift timing, during which information about the individual's behavior or record is known to the system.
When an individual goes over their allowed time, the system and the individual receive notifications.
It will also keep track of how many authorized and unfair people are abiding in the building for more extended than they are considered to.

System for tracking vehicles

One of the most familiar applications of video-based transportation surveillance is vehicle gridlock detection.
In this case, video processing might be used to watch the speed of traffic on highways, which could be used to predict travel time, calculate toll values dynamically, and so on.
A point detection and tracking methodology are used to evaluate the video picture sequence captured by CCTV cameras.
The number of vehicles identified in each frame gets conveyed to the outside world.