Balan­cing Spe­ed and Effi­ci­en­cy in Com­pu­ter Visi­on Ana­lytics: When Fas­ter Isn't Alwa­ys Bet­ter

"Faster is better" – or is it? A computer vision algorithm is often expected to run at real-time speed. But is that really the case? Do we need fast performance in every situation? Often, pushing for real-time processing can lead to unnecessary costs and significant challenges. In this article, we’ll clarify what real-time speed means, explore which use cases truly need it, and discuss why aiming for high speed isn’t always the best choice. Let’s find out how to make smarter, more effective decisions in computer vision!

Understanding real-time speed and its challenges

Algorithms or analytics are considered 'real-time' when they can process incoming data swiftly enough to ensure minimal delay. In the context of Computer Vision (CV), this means the algorithm effectively handles all frames from a live video stream. Because of its speed, it can overlay detection boxes or regions onto the live feed without lag. Generally, an algorithm that operates at more than 15 frames per second (FPS) is considered real-time.

The appeal of real-time CV algorithms lies in their ability to enhance user experience. They display results instantly on the live video stream, preventing dropped frames. This responsiveness leads to a more engaging and dynamic user interface.

However, achieving high algorithm speed and real-time responsiveness comes at a cost:

• Hardware costs: Fast algorithms demand more compute resources, often requiring larger and more expensive hardware to function effectively.
• Speed vs. accuracy trade-off: Compute resources are always limited. As a result, sacrifices in accuracy may be necessary to achieve higher speed and responsiveness.
• Power and battery consumption: Real-time computer vision algorithms that process all frames available consume significantly more power than those that, for example, selectively drop frames. This increased power demand can be especially challenging in battery-operated systems on the Edge.
• Development costs: Optimizing an algorithm requires a high level of expertise, along with source code optimization and AI-related strategies such as quantization, model pruning, and architectural changes. These requirements can significantly increase development costs.

Use cases that demand real-time analytics

Is real-time processing truly necessary for your use case? The answer largely depends on the specific scenario and how quickly the surrounding environment is changing.

Not all applications demand real-time responsiveness from analytics. In cases where immediate responses aren’t critical, it’s advisable to reduce the sampling rate to the absolute minimum required. This adjustment can result in significant savings on hardware costs, power consumption, and battery life, ultimately extending the lifespan of the equipment.

Generally, real-time or fast analytics are essential in scenarios where key environmental factors change fast, or decisions must be taken within a split second. Examples include:

• Gauge monitoring: Quickly opening a pressure valve when tank pressure exceeds a threshold.
• Self-driving vehicles and robots: Immediate response to environmental changes, such as road obstacles or interactions with other vehicles.
• Augmented reality: Ensuring smooth and responsive overlays on a live video feed.

On the other hand, for use cases where target events develop at a slower pace relative to the frame rate of the video stream, only a few data points per minute or even per hour may be enough. Use сases that require less frequent monitoring:

• Liquid Leak Detection: A puddle typically takes several seconds to form and become visible to a camera. In most cases, a sample rate of one frame per second, or even one frame every five seconds, would be enough.
•  Flood Detection: While it’s crucial to identify flooding events as quickly as possible, floods generally develop over several seconds to minutes. Consequently, capturing one frame every minute, or even less frequently, is often adequate. In areas prone to flash floods, slightly higher sampling rates — around two frames per minute — may be advisable to ensure timely detection.
• Fire and Smoke Detection: Due to the nature of fire, which grows exponentially, every second is critical. However, it's not always necessary to process all 30 frames per second from a video stream. In many instances, one to two frames per second may be sufficient.

While real-time speed is often highly sought after in computer vision algorithms, it may not be essential for every application. In use cases where environmental changes occur gradually, such as liquid leak detection or flood monitoring, a lower frame rate can often suffice. This approach not only minimizes the strain on hardware costs and power consumption but also reduces the complexity of development efforts. Ultimately, aiming for speed without context can lead to unnecessary trade-offs in resource allocation and system efficiency. Therefore, adopting a balanced strategy that considers both performance requirements and resource constraints is crucial for achieving the most effective and sustainable solutions in computer vision.

Striking the right balance between speed and efficiency in computer vision analytics is crucial. While real-time processing is critical in high-stakes environments like gauge monitoring or self-driving vehicles, slower sampling rates can be more than adequate for events that evolve over seconds or even minutes, such as liquid leaks and flood detection. By throttling frame rates, you can reduce hardware and power costs, avoid needless complexity, and even enhance accuracy by allocating resources to more robust algorithms.

Noema apps excel in delivering this flexibility. Their speed-throttling capabilities empower you to adapt processing rates to your specific needs, reducing both energy consumption and hardware expenses. Moreover, by running multiple instances efficiently on the same Edge device, you unlock greater scalability. In short, with Noema you gain the agility to prioritize performance where it truly counts, all while keeping costs in check — an ideal blend of speed, precision, and sustainability for any computer vision application.