Applying AI to real-time video processing: The basics and more

Written by Maksym Tatariant, Data Science Engineer at MobiDev.

There is nothing new about the use of artificial intelligence (AI) in video processing. Looking beyond image processing, this is one of the most common use cases for AI. Similar to image processing, video processing uses established techniques such as: computer visionutilizes object recognition, machine learning, and deep learning to enhance this process.

Are you using computer vision and NLP? video editing and generation, object recognition Automatic tagging of video content Tasks, streamlined with machine learning AI video analysisor deep learning to facilitate real-time background removaluse cases continue to increase every day.

Read on to learn what approaches you can take when using AI in video processing.

Fundamentals of real-time video processing

Let's start with the basics. Real-time video processing is an essential technology in surveillance systems that use object recognition and facial recognition. This is also the key process that powers AI visual inspection software in the industrial sector.

So how does video processing work? Video processing involves a series of steps such as decoding, computation, and encoding. Here's what you need to know:

• Decoding: The process required to convert a video from a compressed file to its raw format.
• Calculation: A specific operation performed on raw video frames.
• encoding: The process of converting processed frames back to their original compressed state.

The goal of any video processing task is to complete these steps as quickly and accurately as possible. The easiest way to accomplish this involves parallel work and optimizing algorithms for speed. In simple words? You should take advantage of file splitting and pipeline architecture.

What is video file splitting?

Splitting the video file allows the algorithms to work in parallel, using a slower, more accurate model. This is achieved by splitting the video into separate parts and processing them simultaneously.

Video splitting can be thought of as a form of virtual file generation rather than subfile generation.

Nevertheless, video file splitting is not the best option for real-time video processing. Why exactly? This process makes it difficult to pause, resume, and run. Rewind File being processed.

What is pipeline architecture?

Another option is a pipeline architecture. This process works to split and parallelize the tasks performed during processing, rather than splitting the video completely.

Here we provide a simple example of what the pipeline architecture actually looks like and how it can be used for real-time detection and blur in a video surveillance system.

In this example, the pipeline splits the tasks into decoding, face detection, face blurring, and encoding. If you want to speed up your pipeline, you can use it. deep learning technology.

Decoding and encoding explained

What about decoding and encoding? There are two ways to complete these processes: software and hardware.

You may already be familiar with the concept of hardware acceleration. This process is possible thanks to the decoders and encoders installed on his latest NVIDIA graphics card, as well as the CUDA core.

So what options are available when it comes to hardware acceleration of the encoding and decoding process? Below are some of the more common options.

• Compile OpenCV with CUDA support. Compiling OpenCV with CUDA optimizes both decoding and pipeline computations using OpenCV. Note that the Python wrapper does not support this, so it must be written in C++. However, in situations where you need to do both decoding and numerical computation using the GPU without copying from CPU memory, it is still one of the better options available.

• Compile FFmpeg or GStreamer with NVDEC/NVENC codec support. Another option is to use the built-in NVIDIA decoder and encoder included in a custom installation of FFmpeg and Gstreamer. However, we recommend using FFmpeg if possible, as it requires less maintenance. Also, most libraries utilize his FFmpeg, so replacing it will automatically improve the library's performance.

• Uses the NVIDIA video processing framework. The final option is to use a Python wrapper to decode the frames directly into PyTorch tensors on the GPU. This option removes extra copies from the CPU to the GPU.

Face detection and blur

Object detection models (SSD or RetinaFace) are popular options for completing face detection. These solutions work to locate the human face in the frame. And based on our standards, experience,We tend to prefer the Caffe Face tracking model and the,TensorFlow object detection model as they gave the best,results. Additionally, both are available using his OpenCV library. dnn module.

So what happens after a face is detected? Next, Python and OpenCV-The base system is reveal Bounding boxes and detection reliability. Finally, a blurring algorithm is applied to the cropped area.

How can I build AI-powered live video processing software?

It's no secret that video processing, the codecs that enable it, and both the necessary hardware and software are quite technical in nature.

However, that doesn't mean you can't use these tools to build your own live video processing software.

Here's a quick breakdown of what you need to do:

1. First, tune the pre-trained neural network to complete the required task.
2. Configure your cloud infrastructure to handle your video processing and scale as needed.
3. Build software lawyers to condense processes and integrate specific use cases such as mobile applications and admin or web panels.

Developing a similar video processing software MVP using a pre-trained neural network and a simple application layer can take up to four months. However, scope and schedule vary depending on the details of each project. In most cases, it makes sense to start with proof-of-concept development to explore the details of your project and find the best flow.