Feature

Top Video Surveillance Trends for 2018 – Forensic Video Analytics as a Service

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.

 


 

Josh Woodhouse

Forensic Video Analytics as a Service

When reviewing video for investigations one of the biggest challenges is the sheer volume of video footage which may need to be examined. It’s said that typically it may take a trained officer or analyst using traditional methods (a notepad and the pause/ rewind buttons) 1.5 – 2 hours to review just an hour of raw video footage. This can be a huge consumer of resource. The problem is particularly prominent in police forces where the issue is amplified by a combination of budgetary constraints and a spike in the amount of video inputs (increased use of body worn cameras and more publically submitted videos). There have long been grounds to find a more efficient solution.

Several video surveillance analytic solutions for forensic video analysis have been available for some time, yet the improvement in accuracy in the last 18-24 months using deep learning technology has been critical. This advancement has pushed accuracy to a level of competency that can be reliable enough to assist human analysts. However, deploying this technology can prove expensive for police departments. At present there is a significant hardware cost required to run this type of video analytic. And many of the potential clients are not managing live cameras but instead looking for a tool to search through the repository of potential evidence they have collected from multiple sources in many formats.

Some providers have offered use of their analytics and software packages in a ‘as a service model’ where police forces or agencies can utilize the vendor’s onsite infrastructure and internal analysts to outsource their forensic video analysis. Moving this model to a cloud platform is an obvious evolution where by following some training clients can use on-demand forensic video analytics for particular cases remotely with their own analysts without large hardware investment.

This is an exciting prospect for some smaller forces, which may not have the available capital for their own infrastructure or a large enough case load to justify a large capital expenditure.

For large agencies and police departments with either highly sensitive data and/ or large case-loads, their own onsite infrastructure will most likely be the most cost effective solution.

IHS Markit expects that forensic video analytics will be integrated into existing cloud services. For example in the body-worn camera market many police forces already utilize the cloud to store and review body-worn video, yet, in these repositories we still see a degree of separation from other video sources, for example from fixed (public or private) video surveillance. In 2018 IHS Markit expects to see increased convergence in post recording video repositories where, even if only on case by case basis, multiple sources of video will be brought together to be investigated using deep learning video analytics for which cloud may be an important enabler for on demand requirements.

 

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

The Evolution of Deep Learning in Video Surveillance   By –  Monica Wang

Drone Detection Technologies   By –  Oliver Philippou

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