An excerpt of an article from Security InfoWatch magazine about the trends and changes coming for Enterprise Video Surveillance.
The traditional methods that most enterprise-type organizations have managed their video surveillance systems no longer measure up to the pace of digitalization and the data tsunami most CSOs face on an almost minute-to-minute basis. The strain of moving from analog CCTV systems to network-centric video has essentially made closed video systems and appliances obsolete. The escalating need for speed, expanded processing power and enhanced storage capabilities dictate where an organization’s video system’s roadmap must travel.
Security Technology Executive (STE) recently sat down with both Red Hawk Fire & Security and Western Digital to get a perspective on what’s trending and what challenges are posed for enterprise-level end users who are planning to expand or update their video solutions. This exclusive Sponsored Solutions Provider Roundtable-in-Print draws some interesting perspectives from one of the country’s top systems integrators and a leading video surveillance data and storage vendor. Joining STE are Darrin Bulk, Director of Product Marketing and Client Devices at Western Digital along with Rick Tampier, a Senior Dir. Sales & Product Strategy at Red Hawk Fire & Security and Brandon Cobb, who is a Senior National Technical Project Manager with Red Hawk.
STE: The number of cameras and sophistication of new security video systems continues to grow. How can a security system integrator help you determine the best video surveillance/analytics system for your business?
Rick Tampier: You used to have only a few choices when it came to cameras and software, now the selection can be daunting. By taking an unbiased, consultative approach, the ideal systems integrator can help you sort through the array of new technology choices available and integrate solutions that both fit your needs and are within your budget. It’s important for your integrator to have a clear understanding of your goals or the main issues you need to address by using video and analytics. Are your cameras to be used for general surveillance, “situational awareness” or for a specific purpose such as document identification, facial recognition or license plate recognition (LPR)?
If you know for example, that your camera system needs to be able to identify a specific individual who shows up at your casino, spot the license plate of a prohibited vehicle, or if you need cameras that work in changing lighting conditions, it’s all vital information to communicate with an integrator that knows the best product for each individual application.
STE: What are the biggest technology trends that are shaking up (or will shake up) your markets and how are analytics and Artificial Intelligence (AI) solving real-world problems?
Darrin Bulik: There have been so many advancements over the past few years, from higher video camera resolution to the networking of camera systems to adding in deep learning capabilities, not to mention the increasing adoption of video surveillance cameras. Nowadays, it’s not that unusual to see drones flying in the field for industries like agriculture, or oil and gas, along with the more traditional video surveillance in retail stores and businesses, traffic lights, and large stadiums for example. And a lot of that expansion is to be expected. What’s really changed is how the data is being captured can be used to drive more actionable insights. Three key trends that are currently unfolding in the video surveillance space are AI-enabled systems, deep learning, and a cloud-to-edge perspective on surveillance.
AI and machine learning are key to supporting real-time responsiveness for video surveillance systems. Through machine learning, for instance, systems can be trained and adapted to identify nuances and differences in patterns, shapes, colors, sounds, vibrations and temperature, for example, which are all important and at times critical to help identify and detect issues in real time through applications like facial recognition that may be deployed for advanced identification, verification, search, prevention, response and rescue.
Deep learning takes AI capabilities even further, analyzing video data to extract valuable insights. This is an intense process as you can imagine: Effective deep learning involves higher computing power and can require thousands of hours of “training” with actual video footage to discern one human behavioral pattern. And this deep learning is taking place all along the data path, from data in the surveillance video servers at the “edge” to server-based AI-enabled NVRs, to the cloud through Video Surveillance as a Service.
The location of your data is also key, and the use cases will define this storage strategy. “Big data” is aggregated in the cloud to collect large volumes of data and analyze it over time, and can also be the data captured on NVRs in between the edge and the cloud. This is where deep learning functions operate, for example. On the other end of the spectrum, “fast data” happens at the edge on cameras and other connected devices in the field that needs quicker, even real-time, analysis for emergency situations. Take, for instance, a stadium hosting a large sporting or concert event: If surveillance detects fast movements (such as a car or motorcycle) approaching the venue in an unexpected and atypical manner, fast data allows the system to analyze the footage without any human interaction to flag urgent insights.
Tampier: It used to be that recorded video was used primarily for forensics for law enforcement or prosecution. What we’re seeing today is that with innovations in the technology there are tools that can help us take action in real time to emergencies or other situations.
One example is a large, multi-location company Red Hawk works with that recently began having parts stolen from the vehicles parked at their facilities. Digital cameras with analytics built-in can be used to identify someone crossing the perimeter of the facility combined with the ability to send an audio warning to ‘talk-down’ potentially averting a theft. If the situation escalates authorities can be automatically notified quickly.
Many of our customers are interested in video analytics and remote video because these sophisticated cameras can serve multiple purposes, not just capturing clear images of the property, but being the eyes and ears at ATM locations or documenting regulated employee practices like hand washing in healthcare facilities. There can be real value to improve business operations in addition to detecting, deterring and documenting security risks.
STE: As more video surveillance systems come online, joining the IoT, what are the biggest challenges the industry is currently facing in terms of implementation and deployment?
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