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How Video Analytics Software Solves 3 Common Problems for Cities, CSPs and Businesses

As the costs of cameras has declined, the use of video surveillance has surged. Cities, schools, communication service providers (CSPs) and businesses are using videos to secure their areas or gather business intelligence. Plus, video has become a key player in some of the latest, most innovative applications of the Internet of Things (IoT).

With more video cameras in play, a host of new issues has appeared that is limiting the advantages video offers. This is where video analytics comes in.

Here are three common issues organizations and municipalities face and the solution video analytics software offers.

Problem #1. Increasing Infrastructure Costs

More cameras equal more data. Video surveillance creates about 1MB of data per second – data that needs to be transmitted, analyzed and stored. This requires more bandwidth, more human monitors, and more storage, all of which comes with ongoing costs for the user.

Solution: Cost-efficiencies that Drive Down to the Bottom Line

Video analytics software cuts down on bandwidth, monitoring, and storage needs. It starts with edge computing, where video surveillance footage is analyzed where it’s captured – at the camera. Normal footage that doesn’t require further processing will be discarded. Footage that requires additional processing will be moved to central storage for further analysis. After a second round of reviews, only about 5 percent of footage needs to be stored and reviewed by human monitors.

Problem #2. Pressure on Human Monitors Miss Important Events

Adding more video cameras to a company or municipality’s ecosystem can exacerbate inefficiencies and other issues. Humans are not equipped to watch screens for long periods of time. One study reports that humans can only monitor continuous video effectively for 22 minutes. After that, they’ll miss 95 percent of screen activity.¹ The more cameras are added, the less effective human monitors become.

Solution: Scaled Monitoring with Artificial Intelligence

Statistical algorithms enable video analytics software can process large quantities of data without getting tired. Further, advanced systems are equipped with artificial intelligence like deep learning, so they become better at spotting anomalies in video footage as they process more data.

Problem #3. Reactive Video Monitoring

As said, humans can only monitor so many screens at a given time. This makes many video surveillance systems great forensic tools that can be analyzed after the fact. Unfortunately, it also makes it common for cities and CSPs to focus more on reacting to video later, instead of acting quickly to improve public safety, traffic, and other immediate issues.

Solution: Real-Time Issue Alerts

Video analytics software analyzes data in real-time, flagging anomalies in footage and issuing real-time alerts for review by humans. With this functionality, video surveillance professionals decrease their reaction time, making it possible to improve public safety in cities, schools and businesses.

Use Video Analytics to Increase Surveillance Efficiency

Every organization using multiple cameras runs into problems with reactivity, scale, and costs. Video analytics software is an easy way to fix them. You can add it to existing video systems, including legacy cameras and non-standard systems, making video analytics a simple, fast way to improve your video system.

Turn cameras into IoT sensors with real-time video analytics software.


Referenced Sources:
¹ https://archive.org/stream/IntelligentNetworkVideoUnderstandingModernVideoSurveillanceSystems/Intelligent+Network+Video%3B+Understanding+Modern+Video+Surveillance+Systems_djvu.txt

Case Study: IoT is redefining the customer experience. Nokia case study.

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