How Big Data in video surveillance benefits systems integrators

How Big Data in video surveillance benefits systems integrators
Big Data is making an impact on several fields. According to Wikibon, market revenue from Big Data products and services will grow at a CAGR of over 10 percent in the next ten years or so.  In video surveillance, the impact of video surveillance is becoming more and more evident.

Speaking to asmag.com recently, Eric Olson, VP of Marketing at Puretech Systems, pointed out that there are two major advantages that Big Data could bring to video surveillance.
  1. Robust scenario replay

With the advent of deep learning assisted video analytics, a vast amount of big data details can be collected about real targets, non-targets, and details of the scene. When combined with geospatial command and control interfaces, and sensor collaboration, this big data can be collected, corroborated and run back through the system, providing the user a robust scenario replay of the entire system and connected sensors. This enables security to expedite an incident response, evaluate areas for response improvement, prosecute offenders and train new users. 
  1. Easy calculation of ROI

Calculating the ROI of a surveillance system has been a very problematic task, until now. This collection of big data from video and related sensors also aids business management to measure the effectiveness of their surveillance system. Data collected about the type of activity in a scene can be compared against confirmed alarms, false alarms and nuisance alarms across all available sensors to provide a current confidence level of the entire surveillance system and measure how that improves over time. 

How do security systems integrators benefit from Big Data?

While computing the ROI of a security system is difficult, it is a key factor business uses when making capital expenditures, including security improvements.  According to Olson, integrators can leverage this decision-making bias by focusing on the benefits of a security system big data to help the end user understand the added value.

“For example, big data includes the idea of automatic sensor collaboration,” Olson noted. “That means the security person is not required to perform this task manually, freeing him up for response actions, and over the long term allowing for workforce savings.”

Big data can also provide heat-map type location metrics showing where both real events and false / nuisance alarms occur over time. This can provide the end user actionable data to reduce the cost of real incidents, such as increasing physical barriers or increasing lighting, and where they can take actions to reduce non-events that waste valuable man-hours, including modifying problematic vegetation, making lighting design corrections or improving camera positioning.

Which verticals are keen to adopt Big Data in surveillance?

Retail has always been a major user of big data as it applies to security-focused surveillance and monitoring consumer behavior, according to Olson. As technologies such as IoT drive more sensors to be added into the big data pipeline, retail will consider this added data to both secure their assets and understand their customers. 

“Similarly, industries with critical assets and comprehensive security systems, such as nuclear, power utilities and transit will begin to use this big data to forecast situations, assess current security system performance levels, leverage more robust training and track monitoring improvement and accuracy using big data from their substantial number of safety and security sensors,” Olson concluded.


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