IHS Top Video Surveillance Trends For 2018:

2018년도의 영상 감시시장의 주요 트랜드 내용입니다. (IHS 기관에서 발표)
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IHS Top Video Surveillance Trends For 2018:
 
AI (artificial intelligence) - computers being able to perform specific tasks as well as or 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 being 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, using 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) 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.

Forensic video analytics as a service - 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.