Powered by deep learning technologies, computer vision (CV) presents immense business potential in areas like video surveillance, high-quality product manufacturing, and autonomous vehicles. Besides computer vision, the year 2023 has paved the way for exciting technologies to the mix, like Generative AI.

The coming years hold a lot of promise for AI-powered technologies like computer vision. In fact, AI in computer vision market is set to grow to over $100 billion by 2032. Author and technology advisor Bernard Marr says, “In 2024, we can expect CV algorithms to become increasingly powerful and pervasive, along with the emergence of new and perhaps frightening use cases.”

Here’s a look at five exciting and futuristic trends in this domain.

1. Generative AI

Generative AI is proving to be a “transformational” technology in influencing CV applications. With its ability to generate synthetic data, companies can leverage this technology to “train” CV systems at lower costs and risks to data privacy.

Generative AI tools can also label training data faster than manual labeling performed by human annotators. Generative AI-powered CV algorithms are also more adept at capturing and analyzing 3D images. With 3D imaging, captured data is more accurate for 3D models used in simulations and digital twins.

Also, Generative AI in computer vision can create new “realistic” 3D images and videos useful in real-life applications like architecture and engineering.

2. Augmented Reality (AR) 

Augmented reality essentially “superimposes” computer-generated digital information like 3D models and images in the real-world environment. The development in this space is rapid. In 2024, for instance, we’ll be awaiting the release of more AR-enabled devices from the likes of Meta and Apple. Even today, using face and eye tracking, Meta Quest Pro can closely match a person’s digital image with their real-world facial expressions.

When integrated, AR and CV technologies can deliver new real-world experiences in:

• Object tracking and recognition
• Real-time understanding of the environment
• 3D mapping and depth perception
• Recognition of user’s gestures and postures

3. Edge Computing 

By capturing and processing visual information directly on devices, CV systems can reduce overall operational costs. This is where edge computing holds loads of promises. With edge computing, CV-enabled systems can analyze real-time images in a quicker time without an expensive infrastructure and high-speed Internet connection.

Edge computing enables many CV-powered applications, such as autonomous vehicles and security systems, that need to process real-time data continuously. In 2024 and beyond, we can also look forward to seeing CV models running on low-power devices.

4. Data-Centric AI 

So far, computer vision systems have largely focused on using AI models and algorithms for processing visual data. Among the latest trends, there is a gradual shift from model-centric AI to data-centric AI. With the data-centric approach, CV systems can now use high-quality data for training AI models. This approach provides a better interpretation of visual information and improves the efficiency of the AI models.

In his 2022 address, Dr. Andrew Ng talked about how data-centric AI delivers better results in the following areas (as compared to a model-centric approach):

• Detection of steel defects (78.74%) – as compared to 75.72% in model-centric AI
• Surface inspection (85.45%) – as compared to 85.05% in model-centric AI

This is reflective of the potential that data-centric AI holds for computer vision applications.

5. Metaverse 

To experience the metaverse, users require a host of connected devices, including headsets, haptic gloves, and applications. In this realm, computer vision is playing a crucial role in making the “virtual” world more real.

In the context of CV and beyond, metaverse can extend to several real-life applications such as:

• Virtual tourism, where travelers can have a multi-sensory travel experience (without physically visiting the location)
• Healthcare and wellness domain, where CV-enabled devices can improve patient care and provide technical training to trainees on complicated medical interventions
• Virtual real estate, where virtual users can digitally own (or rent) a piece of real estate in the metaverse

So, What Is Driving the Need for a Computer Vision Platform?

As the number of CV-enabled applications keeps proliferating, organizations need an end-to-end computer vision platform that can implement CV initiatives in a matter of days. A computer vision platform can help centralize and standardize CV-related workflows across the enterprise. 

Further, an efficient CV platform can significantly reduce the time and cost of maintaining implemented solutions by:

• Transforming real-time video feeds and data into a centralized knowledge hub for operational improvement
• Integrating computer vision tools with enterprise systems for faster sharing of real-time visual data
• Training the computer vision models easily and setting them up for successful outcomes
• Measuring and analyzing the performance of AI models and providing granular insights for improvement

Effectively, a computer vision platform significantly reduces the cost and maintainability of the implemented solution.

The Final Word

AI-powered computer vision is being leveraged across industries. Moving forward, we can expect a range of innovations enabled by this transformative technology. 

At KamerAI, we have “actionized” computer vision to deliver business value to many industries. Through our end-to-end CV platform, you can:

• Improve operational efficiency
• Keep your facility workers safe
• Track your product in real-time

Interested in learning how our computer vision solutions can benefit your enterprise? Talk to our experts today.