Industry Professional

Transitioning from Video to Vision

IP-based digital capabilities have surpassed quality and edge processing capabilities

In recent years, video surveillance has experienced a constant and rapid evolution of both technology and use cases for IP video in the traditional life safety and loss prevention worlds. IP-based digital video capabilities have far surpassed predecessor analog video systems in terms of both video quality and edge processing capabilities such as recording, onboard analytics and more. These advances have allowed the industry to prioritize and focus on more useful video monitoring and recordings based on items of interest, rather than blanket recordings and time-consuming post-recording searches. Based on the added intelligence, use cases well beyond traditional physical security have been developing and are on the cusp of rapid adoption of non-traditional uses for network video.

In the context of digital or networked video, the more common terms we hear used are machine vision and computer vision. The distinction between the two is that the camera is keyed in on monitoring specific conditions, processes or items to look for anomalies, such as a paint defects early in an automotive manufacturing process, or ensuring the right pills are put in the right prescription bottle based on visual verification in an automated prescription fulfilment process. We are also hearing the term vision being associated with the terms deep learning and artificial intelligence (AI).

Somewhere in the space between traditional video surveillance applications and the promise of deep learning or AI, we are seeing increasing deployments of smart systems, such as smart buildings, smart parking and smart cities. The two examples detailed below illustrate the potential power of video surveillance for providing deeper vision when integrated into these types of applications.

Smart Parking

Smart parking is a prime example of the evolution of this vision applied on top of traditional automation systems to increase communications, efficiencies and profitability by augmenting available data. A snapshot of smart parking without video would look something like this:

Automated entry gate to provide access and a ticket to track parking duration for billing. Potentially parking space-specific weight sensors to determine which spaces are occupied. Potentially more generalized pressure plates to count the number of vehicles entering or exiting zones or parking levels to estimate the number of available parking spaces. Exit gate with staffed and/or automated pay stations.

What we are now seeing is a vision overlay added to these nonvisual systems, which provides added benefits. For example, video overlay of parking spaces can allow for reservation of specific spaces, as well as license plate recognition (LPR) of the vehicle for which the space has been reserved. This can allow for tiered pricing based on space location and can tie in to retail VIP or loyalty programs, providing automatic notification to a retailer that a special customer has arrived.

Additionally, LPR can be used at ingress points to identify entry attempts by undesirable or blacklisted vehicles. Video can also be used to identify vehicles that have parked in a manner that eliminates the usability of an adjacent parking space.

It is important to note that edge intelligence like LPR capability enables the recognition and transmission of key data points without the need to transmit more exhaustive and very dense video traffic. The increased efficiencies can be apparent and especially valuable when tied in to a range of non-video sensors.

Largely depending on the environment for this use case, traditional forensic video used to track theft, accidents and pedestrian incidents are still attractive and valuable. As an industry, we are also starting to see increased traction in adding additional video and audio analytics to this architecture. These may include audio alerts in an area of interest and automated alerts to security staff, police and other first responders.

Smart Lighting

Another area where we are seeing video transition to vision in nontraditional applications is the integration of video camera with smart street lighting. Smart street lighting has seen a widespread increase in integrated network communications based on technologies such as cellular, Wi-Fi, wired connection and other low-bandwidth wireless technologies such as ZigBee, Z-Wave, LORA and others.

The base need for these integrated technologies is to provide twoway communication to control the lighting by turning it on or off based on ambient levels, as well as to collect data from the lights, including power usage and event trigger information—was the light turned on based on motion, and if so, how often? You will now find that many of the top street lighting manufacturers are not only embedding the communications equipment but are also adding a measure of IP video cameras. The promise of integrated video is the ability to create additional services similar to those referenced in the smart parking example, but also potential traffic analytics, such as a vehicle in a bike lane, illegal parking, traffic flow monitoring, accident detection and much more.

Smart parking and smart lighting are just two examples of use cases where video is serving more as a vision sensor and data collection technology. This is in addition to traditional video use cases or exclusive of recorded video used solely to track event-based data and report these data points back to a larger overall system. From these brief glimpses of how video can transition to vision, potential extrapolations from these use cases should immediately become clear.

For example, there is the promise of larger cloud-based learning systems that can collect vast amounts of disparate data points, including vision-based data in real time, and provide instantaneous status and reporting on baseline anomalies. As a result, video surveillance is moving from real-time reactive systems closer to more predictive systems that can be deployed to solve operational challenges while also helping organizations to mitigate, if not avoid, potential security threats.

This article originally appeared in the September 2018 issue of Security Today.

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    September 2018

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