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 Jon Cropley
The A to I of Video Surveillance Terminology
The past 12 months have seen a range of new terms becoming regularly used in the video surveillance industry. We attempt to provide a brief summary of some of these.
AI (artificial intelligence): Computers are able to perform specific tasks as well as, or even better than human intelligence. In the context of video surveillance, AI is used in the field of computer vision to classify visual images and patterns within them.
Big data: Huge amounts of different information are stored, organized and analyzed by computers to identify trends, patterns, and relationships. In the context of video surveillance, the data could be metadata describing hours of video surveillance footage combined with other data sources to highlight patterns relating to security or business operations.
Cloud computing: Instead of using a local server to store or manage video surveillance data, use a network of internet-connected remote servers. Generally this network has the ability to provide additional resource if and when required from a larger available pool. The available resource may be clustered into a datacenter or network of datacenters. These may be private (entirely or partly owned for exclusive use by specific organization/s) or public (resource accessible to multiple separate users).
Deep learning: A branch of machine learning and subset in the field of AI. Deep learning makes use of algorithms to structure high-level abstractions in data by processing multiple layers of information, emulating the workings of a human brain (a neural network).
Edge computing/ storage: Performing data processing and analytics/ storage closest to the source of the data (normally, in this context, in a video surveillance camera).
Face recognition: When a video surveillance system can automatically match a person’s face against a database of individuals.
GPU (graphics processing unit): A programmable chip specialized for use in image processing. Due to the requirement to be able to simultaneously processing multiple large data blocks required in modern image processing, GPUs have been found to be highly suitable for deep learning/ neural network processing.
H.265 (or MPEG-4 part 2): H.265 is a video compression codec standard approved by the International Telecommunications Union (ITU-T). Compared with H.264, H.265 has the potential to use 30-40% less bandwidth for a video stream of the same quality.
IoT (Internet of things): IoT is not a specific device or technology – it is a conceptual framework, driven by the idea of embedding connectivity and intelligence in a wide range of devices. IHS Markit defines an IoT device as a device which has some form of embedded connectivity that allows the device to be directly connected to the internet (i.e., IP addressable), or allows the device to connect (tether) to an IP addressable device. In the context of video surveillance, this could be using video surveillance data with other sensors or sources of information.