GPU vs. CPU for CCTV Systems: Which Offers Better Value and Performance?

In CCTV analytics, the efficiency and accuracy of your surveillance system largely depend on the hardware it runs on. A frequent discussion in technology circles revolves around which is better: a Central Processing Unit (CPU) or a Graphics Processing Unit (GPU). Each has unique capabilities and limitations, especially when applied to complex video processing tasks. We will explore the differences between CPU and GPU, and how they affect the performance of CCTV analytics. 

What is a CPU?

A CPU, or Central Processing Unit, is the brain of the computer where most calculations take place. It's designed to handle a wide range of tasks from basic arithmetic to complex decision-making processes. However, CPUs are typically limited by the number of cores, which can restrict their ability to process multiple tasks simultaneously.

Pros of using a CPU:

• Versatile and can handle various types of tasks.
• Standard in all computer systems, ensuring universal compatibility.

Cons of using a CPU:

• Limited parallel processing capabilities, making them less ideal for handling multiple video streams.
• Performance may degrade under intensive tasks such as high-resolution video processing, potentially affecting the quality of tasks like Facial Recognition and Licence Plate Recognition.
• Inadequate for running complex new AI models like GPT or large language models (LLMs), which require extensive computational power.

What is a GPU?

Originally designed for rendering graphics in video games, GPUs are specialised hardware designed to process multiple data streams simultaneously. This ability makes them exceptionally well-suited for video analytics, where processing large volumes of video data efficiently is crucial.

• Pros of using a GPU:
  • Excellent at parallel processing, allowing them to handle multiple video feeds.
  • Capable of managing precise models with heavy workloads, accommodating larger camera streams. 
  • Enhanced performance for image and video-related tasks, ensuring high-quality outputs in video analytics.
  • GPUs can reduce the number of servers needed, as one or two GPU-equipped servers are often sufficient. This not only saves space but also simplifies server management.
  • GPUs support the most advanced AI functionalities, enabling your systems to leverage the latest AI tools for better analytics.

• Cons of using a GPU:
  • Typically more expensive than CPUs.
  • Higher energy consumption.

CPU vs. GPU in CCTV Analytics

In the context of CCTV analytics, choosing between a CPU and a GPU can impact the effectiveness of your surveillance system:

• Facial Recognition: For facial recognition, where accuracy and speed are paramount, GPUs provide the necessary computational power to analyse video frames in real time without sacrificing quality.
• License Plate Recognition (LPR): LPR requires the ability to process high-quality images quickly to accurately capture license plate information. GPUs excel in this area by efficiently processing the high-resolution imagery required for LPR.
• Real Time Alerts: Generating real time alerts based on video analytics is computationally demanding. GPUs can manage these demands more efficiently, ensuring that alerts are both timely and accurate, especially on larger camera networks. 
• While GPUs have higher energy consumption, they significantly enhance the number of cameras a single server can handle.  For instance, where a CPU might support 20-30 cameras, a GPU-equipped server can manage over 200 cameras. Increasing camera density per server makes GPUs a highly efficient  for large-scale video surveillance systems. 
• CPUs may appear cheaper per unit, but GPUs offer a better return on investment when considering the hardware cost per camera. GPUs enable much higher quality analytics and support significantly more cameras per dollar, making them a more cost-effective choice for extensive surveillance networks.

Conclusion

While CPUs can perform video analytics, their performance limitations become apparent when faced with tasks that require intensive data processing or real time computation, such as facial recognition and LPR. GPUs, on the other hand, are better equipped to handle these demands, providing not only faster processing but also higher-quality results. For any organisation relying on CCTV analytics to secure premises or monitor activities, investing in GPU technology may offer a more robust, reliable, and efficient solution.

By understanding the roles and benefits of CPUs and GPUs, businesses can better equip their CCTV systems to handle the demands of modern security environments, ensuring faster, more accurate surveillance capabilities. 

As we’ve seen, the benefits of using GPUs in CCTV analytics are substantial, especially in terms of processing capacity and efficiency. At icetana AI, we use GPUs to enhance our video analytics solutions, allowing us to manage a vast number of cameras with greater accuracy and less overhead. 

To discover more about how icetana AI uses GPU technology, please contact us here.