HOW BUSINESSES COPE WITH COVID 19 USING SECURITY TECHNOLOGY

CCTV systems, such as CCTV, are widely used in a variety of configurations including public spaces, public infrastructure, and commercial buildings. Most often they are used for two purposes. Real-time monitoring of physical resources and spaces, identification of safety indicators through collected image information, and safety plan planning In recent decades, the security and public sectors have shown keen interest in people outside these sectors. This interest is mainly due to the increasing crime rate and security threats worldwide driving the continued growth of the video surveillance market. According to a recent report by Mordor Intelligence, the video surveillance market is expected to reach $72.19 billion by 2022 from $29.98 billion in 2016. This market potential has also led to recent advances in information technology that enhance the intelligence, scalability and accuracy of video surveillance solutions.

The development of video surveillance systems is at the heart of the following technology trends:

Big Data Infrastructure
Modern big data infrastructure with 4V big data power such as capacity, speed, versatility and precision opens new horizons for video data storage and access. Collecting large amounts of data, especially high-speed streaming data, from multiple cameras has never been easier. Big data systems provide a way to create and deploy a cost-effective and scalable integrated video surveillance architecture.

Intelligent Video Data Collection
In particular, when signs of security incidents are detected, data collection is accelerated and more comprehensive information is obtained, enabling more accurate and reliable analysis. Big Data Infrastructure Modern big data infrastructure with 4V big data power such as capacity, speed, versatility and precision opens new horizons for video data storage and access. In particular, it collects large amounts of data from multiple cameras, including intelligent data feeds.

Data Streaming Systems
Several transmission systems have emerged in recent years. The latter provides steam management and analytics capabilities that are an important part of the big data systems described above.

Artificial Intelligence (AI) and Predictive Analytics
are significant years in the history of artificial intelligence in the context of growing anxiety. Deep learning techniques like those used in Google’s Alpha Alpha engine. The development of deep neural networks can be used directly in CCTV systems to provide a higher level of intelligence and a more efficient monitoring process. For example, artificial intelligence can perform predictive analytics. This allows security operators to anticipate and prepare for security incidents.

Drones and the Internet of Things (IoT)
This is a combination of IoT devices, smart objects, and video surveillance systems. It also provides critical security and monitoring capabilities for next-generation network security. Drones (or drones) today are used for multi-purpose, inaccessible CCTV operations with a permanent installation of existing cameras.

Convergence of physical and network security measures
We are moving slowly towards the convergence of physical and network security measures. Video surveillance systems play an important role in this integration because they represent an IT infrastructure that can be used to monitor physical areas. This, combined with the flexibility of other network security systems, can provide a comprehensive and integrated approach to security and monitoring.

Cyber Security with the increased use of edge devices
Cybersecurity and protection are becoming increasingly important as the use of advanced tools increases with the use of data collection, storage and security increases as part of smart factories, smart offices, stores and smart or smart city solutions. This becomes even more important when a cyber attack occurs. However, cybersecurity has been and will continue to be a major concern for all applications, regardless of size and complexity. It also has UL CyberSecure Assurance Program (CAP) certification for Wisenet 7 chipsets, keeping sensitive information safe from hackers.

Data Protection and Privacy Respect
GDPR in Europe and CCPA in the US take note of the need for data protection measures for registrants of personal data. This is important. A balanced approach is required to ensure maximum privacy while maximizing the latest technology and data. Hanwha Techwin is doing its best to ensure that users comply with the Privacy Policy. SSM and WAVE video management software, Smart Cover (S-COP) video editing and coverage software, and a video privacy management (VPM) solution that provides full control and compliance over Wisenet cameras and registrations.

CCTV System Engineering
The above technologies offer a new perspective on the development, implementation and operation of intelligent video surveillance systems. However, it is the responsibility of the developers and publishers of video surveillance systems to integrate and maximize the capabilities of these technologies. To this end, it is important to design and implement the right architecture for your video surveillance infrastructure. Modern CCTV system architectures process near-field video information according to a computer/fuzzing-edge model. This saves bandwidth and allows real-time security monitoring. Cameras are located at the edge of the network as part of an end node that can record and process video images. End nodes can also enforce data collection information by setting the frame rate based on a specific security context. It can also connect to cloud infrastructure to communicate, analyze and analyze information from multiple cameras in a short amount of time.

Edge/fog computer architectures are also an ideal choice to support consolidation. IoT drones must be integrated with connected end nodes as part of a mobile edge computing architecture. Real-time stream analytics must be done at the edge, not cloud-based video surveillance. Deep learning capabilities can be deployed both at the edge and in the cloud. State-of-the-art neural networks can support the extraction of complex security models in real time. Extensive security and knowledge models that are simultaneously covered by multiple end nodes (e.g. city-level deployments) can only be mined by implementing deep learning in the cloud. In general, it is difficult to decide whether a particular business should be in the cloud or at the edge. Decisions are usually related.

Implementation Challenges and Best Practices
Video security manufacturers face other challenges in addition to understanding the evolution of their IT architectures. One of these challenges is data protection and data protection compliance. This may limit the type and scope of use of surveillance sensors due to privacy and data protection laws and regulations. The use of drones also requires compliance with existing regulations and another challenge is the degree of automation of the solution. While automation is generally desirable to cover and monitor large areas without additional personnel, inspection and human intervention are still critical for the reliability of the overall solution. There are also other issues related to emerging threats that can arise from the cyber-physical nature of CCTV systems. Combining cyber and physical attacks on video surveillance infrastructure could compromise anyone’s ability to detect physical security incidents. Another challenge relates to the implementation of data-driven intelligence (eg in the context of predictability). analytics and artificial intelligence) require large amounts of data that are difficult to access due to security incidents. Despite the emergence of startups offering advanced artificial intelligence products and services, artificial intelligence is still in its infancy (eg, lightweight and efficient deep neural networks). To address these challenges, video surveillance developers and publishers must take steps or steps to improve standards and compliance. This allows for a seamless transition from manual or human-mediated systems to fully automated visual surveillance powered by artificial intelligence. You also need to start with simple rules and actions and use more and more data-driven intelligence.