GPUs And Video Analysis: How Deep Learning Is Making Video Surveillance Smarter

27th June, 2019 by

The Central Processing Unit, or CPU, is the beating heart of all computing systems. This general purpose processor provides the computational power for most of the tasks a system undertakes. However, CPUs are restricted to conducting certain basic mathematical calculations because they’re typically designed to deal with calculations in sequential order. As a result, more complex tasks may bog a CPU down and make it less efficient.

Also known as GPUs, graphics cards are specialist processors. Once upon a time, they were only viewed as important by gamers who recognized the enhanced processing power on offer. Today, however, most PCs, laptops, and other systems contain dedicated graphics cards to render 2D and 3D graphics. This power is derived from the thousands of cores within a GPU, allowing the processor to perform complex calculations in parallel. And this latent power is now being leveraged in a wide variety of different ways beyond gaming.

 

GPUs and video analysis

GPUs have long been useful when it comes to running high-quality videos on PCs, laptops, or other systems. HD video streams require the decoding of compressed data to produce high-resolution images, demanding an amount of processing power that many CPUs could not cope with. Next time you’re watching Netflix in 4K on your computer, you can thank those GPUs.

GPUs are also best suited to transcoding video from one graphics format to another, which also uses plenty of processing power. To burn a digital video onto DVD, for instance, the video data must be re-rendered into an entirely new format. However, the area of video analysis is where the possibilities introduced by the use of GPUs are most exciting. When it comes to GPUs and video analysis, deep learning is key. This is an advanced machine-learning technique related to artificial intelligence (AI). In the case of GPUs and video analysis, it can be utilized to empower AI cameras with enhanced processing power, and it allows them to analyze video and deliver insights in real time.

Deep learning involves extremely complicated mathematical and statistical computations, which are beyond the scope of this post (and author). Suffice it to say, GPUs are the only processors with the power and capabilities to handle those computations. Their ability to perform parallel calculations lets them process massive amounts of data, and this has some exciting real-world applications in terms of video analysis.

 

Real-world applications of GPUs and video analysis

By leveraging the power of GPUs, AI cameras can be trained to perform a variety of invaluable tasks. By way of deep learning, they are able to monitor and understand video footage at phenomenal speeds. They can then pull together massive data sets and deliver rapid insights far more quickly than traditional methods of video processing could accomplish.

This opens up a multitude of possibilities for video analysis. It means cameras can be instructed to recognize certain objects or to identify patterns of movement and interactions. They might be trained to immediately recognize a gun or other weapon, or be used to track and classify objects like vehicles and pedestrians. That makes them effective in traffic management, or even for identifying illegal traffic maneuvers.

High-end applications for AI cameras powered by GPUs are even more impressive, including facial recognition and crowd behavior analysis. All this is made possible by the parallel processing power of the humble GPU. The processors once seen as only important to gamers are now key to some truly remarkable technological advances, and will surely play a vital role in our society going forward.