Why is computer vision so difficult?

  • AI vision encompasses techniques used in the image processing industry to solve a wide range of previously intractable problems by using Computer Vision and Deep Learning. However, high innovation potential does not come without challenges.
  • Real-world use cases of computer vision require hardware to run, cameras to provide the visual input, and computing hardware for AI inference.
  • Even with the promise of great hardware support for Edge deployments, developing a visual AI solution remains a complex process.

Computers are supposed to be good at processing numbers and doing math, so why is computer vision such a challenging problem that still faces low accuracy rates in many applications? While computer vision has made remarkable strides in recent years, it remains a complex and challenging field due to the variability of visual data, the intricacy of objects, computational constraints, ambiguity in interpretation, data limitations, adaptation to new environments, and ethical considerations. 

Also read: Intel develops the largest neuromorphic computer system

Computer vision use cases depend on edge computing

Artificial Intelligence, especially in computer vision, is transforming industries, powering applications like intrusion detection and crowd analytics in Smart City solutions. However, challenges such as high processing demands for real-time tasks and costly cloud deployment hinder widespread adoption. Edge AI emerges as a solution, moving processing tasks closer to data sources, enabling real-time analysis, cost-efficiency, and enhanced data privacy. This shift addresses the complexities of computer vision, such as variability in data, computational constraints, and ethical concerns, while making applications more practical and scalable.
Also read: What is an example of a supercomputer?

Hardware is a big consideration

Real-world applications of computer vision rely on hardware for processing and cameras for visual input. For mission-critical tasks demanding near real-time analytics, deploying AI solutions on edge computing devices is essential to overcome latency limitations. Take, for instance, a farming analytics system used for animal monitoring, where delays could significantly impact livestock. With each camera feed generating 30 images per second and an average setup of 100 cameras, the data load is immense—nearly 259.2 million images per day. Edge computing eliminates the need to send all this data to the cloud, preventing bottleneck issues and unexpected cost spikes. By running AI inference at the edge in real-time, only crucial data points are communicated to the cloud backend for further analysis. This approach, leveraging advanced Edge AI hardware and accelerators like Intel NUC, Nvidia Jetson, or ARM Ethos, ensures scalable and efficient AI vision applications.

Complexity of scaling computer vision systems

Developing a visual AI solution, even with advanced hardware support for Edge deployments, remains a complex process. Key challenges include collecting specific input data, expertise with Deep Learning frameworks, selecting appropriate hardware and software platforms, optimising models for deployment environments, managing deployments to distributed Edge devices, organising updates across endpoints, monitoring metrics in real-time, and ensuring data privacy and security.

This approach entails significant development risks due to factors such as development time, required domain expertise, and the complexities of building a scalable infrastructure.


Aria Jiang

Aria Jiang, an intern reporter at BTW media dedicated in IT infrastructure. She graduated from Ningbo Tech University. Send tips to a.jiang@btw.media

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