21 December 2021

The security and surveillance industries can’t start looking ahead to 2022 without first acknowledging the significance of 2021: a year that changed every aspect of our daily lives, including how we socialise, work, communicate and collaborate. The world emerged from an unprecedented global pandemic, with companies in every industry re-evaluating every aspect of their business, from how they interact with their customers to how they manage their workforces to how they go to market. This new business landscape has also created new types of security and surveillance challenges. Employees, customers and partners are working increasingly in remote locations, sharing and collaborating through disparate online networks and leaving data vulnerable to intrusions. Sites are being monitored remotely, and new public health and safety guidelines are governing how business operate. Hanwha Techwin has responded to these challenges with new products and solutions incorporating continually emerging technologies from the cloud and automation to Artificial Intelligence/deep learning and an array of connected services and processes. Here’s a preview of how these trends will continue in 2022: Surveillance and security solutions are increasingly incorporating on-board analytics delivering data that can drive intelligent protecting and monitoring. The role of on-board analytics will continue to expand significantly in 2022 and beyond, as customers combine edge computing and Artificial Intelligence (AI) to achieve enhanced monitoring and search efficiency. Industry reports predict the total global edge computing infrastructure will be worth more than $800 billion by 2028. The use of Edge AI, especially with analytics based on deep learning algorithms, can be a key element in a range of “smart surveillance” applications. These include object detection and classification, and collection of attributes in the form of metadata – all while reducing latency and system bandwidth burdens and enabling real-time data gathering and situational monitoring. These benefits of edge computing can only be achieved by having a core competency in SoC. Codecs embedded in SoC play a key role in improving image quality while the NPU engine in the SoC with AI algorithm enables AI analytics on the edge. And due to limited resource availability on the edge, the importance of SOTA (State-of-the-art) AI algorithms is on the rise. Maximising the resource efficiency has always been a top challenge for the edge computing, making the optimisation of the AI algorithms inevitable. To cope with the challenge, new ways of machine learnings-such as Transfer Learning-are presented to replace the current leading algorithms. Getting ahead of the trend and internalising these competencies are the keys to gain a firm ground for the unrivaled edge computing. AI and edge computing will continue to improve the efficiency and effectiveness of network video surveillance systems, applying analytics (object, loitering, virtual line and area crossing detection to name a few) to monitor every type of area or situation. With this pan-vertical AI and edge computing enabled by cameras, users can conduct “pre-emptive detection” and rely less on reactive monitoring, increasing safety and efficiency.   Media contact Rebecca Morpeth Spayne, Editor, Security Portfolio Tel: +44 (0) 1622 823 922 Email: editor@securitybuyer.com