Top 4 Computer Vision Trends To Look Out For In 2021 - OptiSol Australia

Top 4 Computer Vision Trends To Look Out For In 2021

Not less than a year ago, The World Health Organization WHO has stated the novel coronavirus COVID-19 outbreak as a pandemic, disrupting businesses and humankind in an unprecedented way. COVID-19 has changed the dynamics of every aspect of our life.

As the popular saying goes “In the middle of difficulty lies opportunity” – this pandemic has provided several opportunities for businesses to adapt and to evolve.

If I am asked to pick the best thing that has happened to multiple industries during this pandemic, then my money is on “Digital Transformation“. It is no more luxury for industries to overlook the transformation and move toward digital practices. Digital Transformation is happening rapidly – Rapid in a way that the transformation was expected to happen in a decade or so took place within a year.

The acceleration will continue through 2021 and there is a huge potential in the field of AI and ML so to speak. Artificial intelligence with computer vision technology is the technology combination that will make big in the coming years.

Global Computer Vision Market - OptiSol Queensland

The combination of technologies is becoming increasingly popular already on many fronts such as consumer drones and autonomous and semi-autonomous vehicles. Computer vision along with deep learning technology will be influential in various industries, including education, healthcare, robotics, retail, and manufacturing.

The growth is simply because of the advancements in smart camera technology, and the increasing penetration of smart camera-based computer vision systems. Smart camera-based computer vision systems are cost-effective, compact, and flexible; thus, it becomes easier to implement changes in these systems based on revised regulations and standards.

Computer vision Solutions Market - OptiSol

After being a research technology for the past few decades, computer vision is now being commercialized in a wide range of applications including security and surveillance, automotive, consumer, industrial, medical, and entertainment, to name a few.

The global computer vision market size is expected to reach USD 19.1 billion by 2027, according to a new report by Grand View Research, Inc. The market is anticipated to expand at a CAGR of 7.6% from 2020 to 2027.

Top 4 Computer Vision Trends:

Image Processing:

Workplaces are facing challenges presented by COVID-19, and safe organizational culture is the new buzzword across the globe, and it is considered a more important factor than ever. The world is slowly reviving and adapting to the new normal. To quickly adapt to this new normal, industries must prioritize workplace safety and ensure the workforce is prevented from any health issues.

Artificial intelligence (AI) and machine learning (ML) have gained more traction than ever and it is the go-to technology as of now to strengthen the workplace safety mechanism. Computer Vision (CV) provides the end-to-end solution from monitoring to analyze, respond to safety, and health-related risks. The combination of AI technology and computer vision bridges the gap between humans and the insights that enterprises need today, especially during this pandemic scenario.

The new normal – Social Distancing, organizations across the globe are seeking CV support to track and monitor the movement of people within their workplaces and away from workplaces. CV technology helps firms to analyze employees’ footage during shifts to make sure the standard protocols are followed.

There are higher chances of injuries and subsequent financial losses if the workforce is not wearing PPE – this leading to fatal injuries and loss of lives. Monitoring workers for wearing personal protective equipment (PPE) comes in handy with computer vision.

Automation of PPE detection helps industries to minimize the risk of accidents at workplaces while improving operational efficiency and providing analytics on workplace safety.

Recent developments in the field of training Neural Networks (Deep Learning) and advanced algorithm training platforms like Google’s TensorFlow and hardware accelerators from Intel (OpenVino), Nvidia (TensorRT), etc., have empowered developers to train and optimize complex Neural Networks in small edge devices like Smart Phones or Single Board Computers. This has led to a profusion of initiatives to use such trained models in the domain of Health and Safety (HSE) at the workplace.

Edge Computing:

The biggest revolution in 2021 will be edge computing. Computer Vision tech integrated devices can have multiple use cases. Edge computing is nothing, but the technology attached to physical devices/machines. Once attached, these edge devices allow the data to be processed and analyzed where it is collected. Industries with a higher ratio of network outages can use this technology to its fullest.

Recent advancements and innovations in edge computing are providing answers to the problems of network accessibility and latency. Edge computing for computer vision has responded even better in real-time by moving only the relevant insights to the cloud for further analysis.

An Internet connection and the cloud aren’t generally an assurance, and, in such scenarios, edge computing comes in handy. With edge computing technology in place, devices can be set even in remote areas where a network connection is not available or poor. Not only that, Moreover, edge computing can cut down on expenses spent on the maintenance of cloud computing for data sharing.

When it comes to the implementation and usage of edge computing, Industries, deploy software on edge and then automates cycle time for monitoring the labor-intensive processes. The Edge Box connects to multiple cameras in the work premises and detects occurrences in near real-time. On top of that, it also automatically records the data across the entire work floor to obtain a complete picture of the productivity of the floor.

Using edge computing engineers and people from multiple teams/business units and departments can examine every step in the manufacturing process with Video Analytics. By doing so, one can save hours of human labor and identify bottlenecks in real-time.

In 2021, video analytics software on edge will be the key for businesses that are looking for faster computation, high data security, and real-time insights.

According to Gartner, “The global computer vision market size was valued at USD 10.6 billion in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 7.6% from 2020 to 2027.”

VR & AR Reality:

Augmented Reality and Virtual Reality together are called a mixed reality/merger reality. No matter how it is called (or) described in the industry, this combination of AR and VR technologies is here to stay for a longer-term and predicted to be a game-changer. Computer vision tech enhances the usability of AR and VR and CV is the key to getting the AR and VR into the next phase of advancements. 2021 will be an interesting year since the world is expected to witness more development on this combined technology.

In today’s technology and given its advancements in various industries, Augmented Reality and Virtual Reality technology with the help of cameras and sensors available can map the environment and gyroscopes to position the user can perform activities such as,

Help users overcome obstacles such as walls, human interference, object interference from users’ movements.

Help users adapt to the Virtual Reality environment by detecting eye movement and body postures.

Help users in providing guidance and directions while they are inside a building, moving in public spaces, subways, and more.

There are few stores across the globe that have implemented AR/VR technology already. They make customers borrow an AR device and with the help of the AR devices, customers can make their shopping list, navigate to each item in the stores. The Augmented Reality devices read and record floor plans, stock information, and mapping of the environment in real-time to give accurate directions for users to navigate in the right direction keeping them out of harm’s way.

CV as a Service:

CV as a service is a cloud-hosted technology. CV on the cloud will provide an opportunity for organizations to subscribe to the services rather than building an end-to-end CV platform on their own. This CV as a SaaS model can provide on-demand access to algorithms and APIs under a pay-as-you-go model. This model will be a path breaker in the coming years making the innovation both affordable and scalable to end-users.

CV as a service is predicted to be one of the keys and vital pieces of a business automation process. Not only that, in addition to CV being available on a SaaS model, CV models will run on the edge devices, allowing them to be integrated and available on a greater number of devices.

As of now, industries find it really hard to implement video analytics solutions since its hardware upgrade costs huge. This is seen and considered as a bottleneck of implementing this technology across the globe.  CV through the SaaS model will help industries to overcome this challenge and start implementing it. Industries now can use the available video analytics software’s and integrate them with existing infrastructure to provide insights on the go. These available software’s are customizable and easy to scale, saving both cost and time.

Computer vision apps can also be custom built with advanced components such as object classification, feature recognition, pattern recognition, etc. This advancement in technology will help industries address their business challenges.

Key benefits offered by cloud technology to computer vision are mentioned below:

  1. Access to on-demand CV algorithms or services
  2. Access to APIs for creating CV applications
  3. Pay per-use (or) Pay as you go models both for computational resources and algorithm
  4. Pay per-use (or) Pay as you go models for other intellectual property access.

2021 will witness more advancements in computer vision-based software’s. These advancements are predicted to dynamically change any camera to AI model mapping at any time.

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