Real-Time and Near-Real-Time Video and Image Processing

15 Jun · 5 min read

Real-Time and Near-Real-Time Video and Image Processing

The ways of processing data are constantly evolving. Earlier, while it would take several minutes to process big data, this duration is barely noticeable today. We no longer have to think about delays from data processing. 

Yet, the advancements have started a new race for the most efficient way to process data, especially for images and videos. With real-time, near-real-time, and batch processing options, it all boils down to your priorities and tools. 

This article explores real-time and near-real-time image and video processing, their applications, challenges, and the best way to implement them. 

Real-time Image and Video Processing

Real-time processing requires a continuous inflow of data to process and provide a steady output. It is often seen in scenarios where immediate processing is critical, such as customer care systems and ATMs. 


  • For face detection, the goal is to locate and measure the locations and dimensions of a known set of faces. Frontal human faces are the primary focus of various face detection algorithms. On the other hand, it attempts to tackle the trickier issues with multi-view face identification. Due to the urgency of authentication, face detection is often processed in real-time.
  • Biometric verification refers to automatically identifying and recognizing people based on their features or actions. It can also be used to identify individuals in groups being monitored. The goal of employing such a method is to restrict access to the offered services to just authorized users.
  • In remote sensing, it is necessary to process acquired information signals in real-time. These signals are received from an object or phenomenon utilizing various wireless real-time sensing devices and not in direct contact with the object or phenomenon (such as aircraft, spacecraft, satellite or ship). Remote sensing includes using an ultrasound identification system, Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), X-rays, and space probes.

Near-real-time Image and Video Processing

Near-real-time processing is preferred when the processing time is longer, but the output needs to be quick. This is often used in scenarios where the data sets are significant but need to be critically processed, such as intruder detection in networks or generating leads in sales. 


  • Computer vision is a branch of science that deals with building artificial systems capable of acquiring information from visual data. Video signals, numerous camera perspectives, or data input from medical scanners are all examples of picture inputs. An industrial robot, an autonomous vehicle, or a visual surveillance system are all examples of computer vision applications. The tasks handled are not time-critical, such as organising data, indexing databases of images and image sequences, object modelling and inspection, medical image analysis, etc.
  • In biomedical image enhancement and analysis, the goal is to improve the quality of biomedical images for diagnostic purposes. Imaging modalities previously analogue, like endoscopy and radiography, can now be digitally enhanced, making them more helpful in diagnosing disease.
  • In character recognition, an image of handwritten or printed text (often acquired by a scanner) is mechanically or electronically translated to text that may be edited on a computer. Researchers often use near-real-time processing for character recognition in pattern recognition, artificial intelligence, and machine vision.

Role of AI/ML in Image and Video Processing Projects

Artificial Intelligence (AI) and machine learning (ML) can speed up data processing and improve quality. The use of AI platforms can be helpful in object detection, facial recognition, and recognition of text and images. ML algorithms can interpret the image and video data in the same manner that our brains do. AI and ML are often used to pictures on our smartphones and to automate self-driving automobiles.[Text Wrapping Break]AI and ML can play a significant role in video processing projects as they have a lot of benefits, as follows: 

  • The final results with AI/ML processing are often faultless.
  • The results will be more reliable because the human aspect has been entirely/partially eliminated.
  • Operational costs are reduced.
  • Computer vision-based applications have reduced time to market as developmental time is reduced.
  • ML can also be helpful in learning and improving the customer experience when receiving products and services., has recently used AI and Computer Vision in several image and video-processing projects. When one of their clients came to them with the requirement of a football-tracking app, they followed a DevOps methodology to create a high-tech mobile app. Expert developers carried out the work in an independent and interdisciplinary team. The primary areas of their involvement relied on their experience with Data Science with Python, Image Analysis expertise, and DevOps support. 

Building an image/video processing solution in-house

The image/video processing industry is relatively new yet significantly in demand. While hiring is already pretty tough, looking for talent in this niche skillset makes this even harder. 

Most real-time and near-real-time data processing requirements are used in time- and security-critical applications or software. So, it is crucial to have at least 2-3 experts on the topic. 

But, as this skill set is highly sought after, the best in the business are already employed with the leading companies. Also, it can be pretty expensive to hire a full-time expert for just one project or feature build. 

The above are crucial reasons many companies choose to outsource image/video processing projects. 

Outsource to improve your software quality

As mentioned above, you need a team of experts to handle projects like these. Thousands of software companies worldwide take on short-term and long-term projects for clients just like you. Many of these vendors have expertise in specific industries and technologies that might be precisely what you’re looking for. Hiring such vendors for the short-term eliminates the hassle of hiring an in-house team and reduces your costs. While an in-house team can cost you the salaries of multiple employees over multiple months, you only pay for the project's duration during outsourcing. 

Outsourcing also helps reduce project planning and management struggles, as the right partner is already aware of the complexities of the project and can help you plan more effectively. 

DAC.Digital, are a team of engineers and problem solvers with expertise in various technologies and industries, especially in deep-tech and emerging-tech. Feel free to reach out to them for your next project. 

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