Demand for professional video surveillance cameras has been growing quickly and is forecast to continue growing in 2018. It is estimated that less than 10 million surveillance cameras were shipped globally in 2006, which grew to over 100 million in 2016, and is forecast to make over 130 million during 2018.
Despite this increase in demand, the average price of cameras and other video surveillance equipment will continue to fall quickly. As a result, IHS Markit forecasts that in terms of US dollar revenues the world market for video surveillance equipment will grow at an annual rate of less than 6% in 2018.
It will be challenging for vendors to continue to grow revenues and margins, but there will be opportunities for well-placed vendors. For example, both the South East Asian and Indian markets are forecast to grow at higher than average rates. There is also great potential for the next generation of products powered by technologies like deep learning and cloud computing.
So, what will be the big stories during 2018? Deep learning, GDPR compliance and drone detection technologies are just some of the trends discussed in this eighth annual trends IHS white paper. The following articles are designed to provide some guidance on the top trends for 2018 in the video surveillance industry.
By – Monica Wang
The Evolution of Deep Learning in Video Surveillance
In last year’s edition of our annual trends white paper, ‘Top Video Surveillance Trends for 2017,’ it was discussed that the biggest challenge for mass adoption of deep learning was the ability to demonstrate a security or business intelligence benefit to using the technology in the many different surveillance scenarios. 2017 witnessed great progress in the market with a transition from proof of concept deep learning algorithms to video surveillance products and a whole range of new entrants in AI chipset offerings. With the technology’s concept more proven, future success will depend on the ability to demonstrate a return on investment from deployments.
Driven by the R&D investment from chip vendors, software startups and major video surveillance vendors, deep learning video analytic algorithms have been developed into fully deployable products with user-friendly interfaces and scenario-focused solutions. For example, deep learning face recognition algorithms are now available in search engine type applications, designed to find missing people from video footage.
Transitions such as these are evident in the Chinese market and in the products shown at the recent China Public Security Expo (CPSE) 2017. Full deep learning products on display were either software-based applications with deep learning or video surveillance hardware with embedded algorithms. As an increasing number of vendors develop deep learning algorithms, several software startups have also developed their own deep learning video surveillance hardware to cement their place in the market.
Transformation in deep learning cameras
Following the transition from analog to network cameras, the next stage will likely be a mass market transformation to deep learning enabled cameras. During the transition to network cameras, growth in shipments was accelerated due to large price declines. The worldwide average price of network cameras in 2016 was around one quarter of the 2010 level. A similar trend of large price decline catalyzing a rapid increase in unit shipments can also be expected for the future generations of deep learning enabled cameras.
So far, most of the deep learning cameras sold have been for safe city projects run by police departments in China. These projects are less price sensitive than the remainder of the market, where the average price is still too high for end-users. The cost of semiconductors which enable the deep learning algorithms to run in the cameras are a major component of camera prices. Following the release of deep learning cameras with Nvidia and Movidius chip solutions, more semiconductor vendors (including some from the mobile device market) are highlighting their ambitions for the video surveillance market. Some of these vendors include XILINX, DeepHi Tech, Intel, Vimicro and Qualcomm. These new chip vendors entering this market are increasing the number of options available for the deep learning ecosystem and importantly are increasing pricing pressure at the chip level. This will enable a rapid reduction in the average price of deep learning cameras.
Outlook
Deep learning and AI are now more established buzz words, particularly in China, and the education of the market regarding the technology continues to increase. End-users are becoming more familiar with real world product deployments rather than just prototype demonstrations of an algorithm. Chinese vendors have begun to promote their deep learning products to the rest of the world. 2018 is set to continue this trend with increased sales from the Chinese vendors outside the Chinese market and more case studies from installations outside China.
The year ahead will also see greater differentiation of video analytics products based on an increased number of semiconductor vendors’ chipsets. Besides the initial projects in city surveillance and transportation, more installations in retail and commercial buildings are likely to be the next to embrace the greater use of deep learning technology. As we’ve seen in the wider video surveillance market, a targeted vertical approach is likely to be a common strategy. Vendors that market vertically-focused deep learning applications aligned with their own existing portfolios should have good opportunities to grow.
IHS Analyses
The A to I of Video Surveillance Terminology By – Jon Cropley
Big Differences between the Chinese Market and the Rest of the World By – Jon Cropley
General Data Protection Regulation (GDPR) By – Josh Woodhouse
Video Surveillance Fault Tolerance By – Josh Woodhouse
