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How Do I Implement A Data-Rich Video Surveillance Strategy?


On October 11, 2020 By Team CalAtlantic

Videos provide a lot of value to a company’s safety and security. In this age of digital information, there are many data sensors such as cameras and live-motion sensors that you can integrate into video surveillance for better results. The question is, how exactly can you implement that strategy successfully?

Video surveillance is an integral part of an organization’s security. With today’s advancements, it’s easy to search through hours of video footage and use different perspectives to obtain the information you need. Thanks to video cameras, arguably one of the most productive data sensors invented, videos can help a business keep its assets and employees safe and protected.

The evolution of technology has dramatically increased the possibilities of video surveillance. Video cameras can be used for more than security purposes, such as deep learning, analytical services, and enabling artificial intelligence. It would be a shame not to explore how video can be used together with data sensors to collect and analyze information better.

To successfully implement a data-rich video surveillance strategy, we need to identify the users and how they use the video.

Who are the consumers of video surveillance technology?

As mentioned earlier, the primary users of video surveillance are security professionals. However, many new players share the same interest in this growing innovation. Several businesses use video data and analytics to find out information about their customer’s expectations on their products, measure customer satisfaction, and gauge customer service efficiency.

Marketing experts can test advertising strategies and how people react to them using video data. Even the legal department can use the information provided by video surveillance, as a video that contains evidence against a customer’s claims can save the company thousands of dollars in damages and salvage the reputation of the brand.

Videos can be used with other data sensors to reach their maximum potential.

Due to its promising capabilities in enabling deep learning analytics, videos can be considered a data enabler. Take, for example, the healthcare industry. Video surveillance is a crucial part of keeping the patients safe inside the hospital, as cameras can catch any wandering patient in the hallways and have the assigned video officer send the necessary information to authorities for proper action.

The efficiency of how people can better handle urgent situations is a direct result of how beneficial videos can be when used towards a collaborative strategy.

Now imagine a world where a person won’t even need to monitor the videos in real-time to get information. Through artificial intelligence (AI) and analytic technology, the potential to create an automated video surveillance system becomes more realistic. Instead of waiting for a security officer to decide based on what he (or she) sees in the video, the AI can automatically send communication to all relevant stakeholders in the situation, from corporate security to local police force.

This ability to create a streamlined, automatic response can serve as the pinnacle of data-rich video surveillance.

Our current technology allows analytics to send information to a human operator once a particular trigger has been met. The future goal is for the data systems to develop a decision-making capability to deal with situations on their own and for humans to view the videos afterward only as a means to check the quality of that decision-making for further system improvements.

The objective is to create a unified platform that utilizes multiple data sources.

In an organizational setting, video is central to all incoming data. However, the data sensors that feed it are scattered across facilities and buildings. These sensors were deployed to their locations for different purposes, but they can be connected to the same surveillance system as long as they generate data.

To build a cohesive surveillance system, the data that comes from these sensors should be appropriately displayed. However, many companies have found it challenging to streamline the information generated into a single platform. As such, more businesses are looking to invest in deep learning and data-driven technology to build the proper display successfully.

This unified platform that companies are building towards won’t be limited to looking at video feeds, but will also be capable of opening door locks, connecting to social media and news outlets, accessing system controls, understanding opportunities when decision making is necessary, and making the right call in such situations.

Creating a single-management platform also makes it easier for management to access and control everything simultaneously. An enterprise-level video surveillance system should ideally allow users to view the information they need anytime they want.

A final word

For a data-rich surveillance strategy to work, it must truly recognize the needs of the customer. You can’t create technology to address a need that you have not identified yet. Innovation thrives when you focus on a specific purpose. It is best to maximize technology by solving the needs of the customers at the moment while keeping an open eye to potential improvements in the future.

CalAtlantic Surveillance Systems are highly effective because they customize their services to meet every customer’s specific needs. By using adaptable security systems from CCTV analog to an IP-based surveillance environment, customers can choose from many cost-effective options that can better suit their business demands.

(Source: Security Info Watch)


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