ISC West 2017 was impressive, with many interesting products and new technologies – where GPU and deep learning took first place in prime time. Participating at NVIDIA’s booth and getting a first glimpse into exciting innovations, certainly highlighted the potential of video analytics powered by GPU and deep networks.
With the ability to effectively process highly parallel computing tasks, such as video and graphics, today’s availability of GPU accelerates the process of deep learning, enabling real advancements, which until today could only be imagined.
By applying GPU-based deep learning techniques to video analytics solutions, the market can expect faster processing of video alongside richer metadata. This combination will enhance the quality of object extraction and provide new applications that will be beneficial across many more verticals and use cases.
Video analytics solutions, strengthened by deep learning, enable covering an entire scene for full object tracking at the highest level. Cost-efficient subscription-based cloud services for home and SMBs, trends over time, connecting to other applications, implementation with small appliances, cross camera search and family member identification are just some examples of possibilities the future holds.
Furthermore, we can expect to see performance and accuracy improvements in:
What’s clear from ISC West, is that expectations from deep learning are high – and rightfully so. GPU-based deep networks particularly facilitate making sense of actual video content. This means video can be searched by the content that is actually displayed in the video.
Video remains the strongest sensor and metadata is still king. Leveraging deep learning capabilities, VIDEO SYNOPSIS® innovation and analytics can deliver more value to various markets, from law enforcement and security to safe and smart cities, campuses and retail.
Interested in learning more about GPU processing for video analytics? Check out CTO Tom Edlund’s latest blog post about cost efficient video processing using GPUs.