Video analytics promises tocan boost security efforts by automatically alerting personnel to take action

Video analytics enables security teams to take action as soon as incidents occur

Video analytics can boost security efforts by automatically alerting personnel to take action when a security event occurs. Intelligent sensors never tire, can cover large distances and “see” what the eye would miss, even in absolute darkness. Based on such intelligent analytics, people can make smart decisions when actual violations happen. John Romanowich, CEO of SightLogix believes that the key to effective application of video analytics in the outdoors is to use best practices in equipment selection and installation, as he explains below.  

Reducing outdoor security false alarms

Outdoor surveillance cameras, which operate by detecting movement within their field of view, must contend with an environment that is constantly changing. These cameras are usually mounted high on poles that shake due to slight wind or vibration. Clouds create moving shadows on the ground. Trees and leaves flap in the breeze, creating more movement. Adding in rain, snow and dust, such a dynamic environment can cause an abundance of nuisance alarms that quickly overwhelm security efforts.

Discerning legitimate targets from natural motion is a significant task considering the amount of data that a surveillance camera needs to analyze over a large outdoor scene spanning hundreds of meters.

Doing so is best accomplished with security cameras that bring a high degree of video processing to analyze the scene. When such processing capacity is placed directly within the camera, 100 percent of the raw scene data becomes available to the video analytics system, making it possible to examine the full visual detail of every video frame, eliminating - at the source - all the impediments that would otherwise trigger false security alarms. 

Intelligent surveillance camera with multiple processors
View large image
Figure 1: Intelligent surveillance camera with multiple processors

Figure 1 illustrates this approach, which requires multiple processors embedded into the surveillance camera. This allows the camera to electronically stabilise the image to eliminate camera motion, dynamically correct for changing lighting, fog, rain, snow and sandstorms. It also helps to eliminate motion from small animals, blowing debris, trees moving in the breeze and reflections from water, while increasing the probability that pedestrians or other objects of concern will be detected.

Applying video analysis outside the security camera

Alternatively, systems that employ video content analysis outside of the camera must compress the scene data for network transmission, removing most of the finer scene details. In such systems, often as much as 99% of the data is removed. In the outdoors, the loss of so much detail proportionally degrades the ability to accurately detect targets. Such systems also lack the necessary image processing to compensate for the outdoor variables.

Integrating the imager directly with the video analytics system- inside the camera enclosure, with a high degree of processing the edge - is the cornerstone of a smart camera's ability to accurately detect targets in the outdoor environment.

Pinpointing security violations at hey unfold

Intelligent surveillance cameras can meet these goals when they are geo-registered 
 Geo registration enable security breaches to be located as they happen

The goal for security is to maintain awareness of risks and quickly obtain reliable information about the place and nature of an intrusion. Intelligent surveillance cameras can meet these goals when they are "geo-registered." Geo-registration, which means that the camera's field of view (FOV) maps the GPS coordinates of all points in the landscape under surveillance, unlocks key functional benefits of an intelligent video surveillance system such as situational awareness and automatic PTZ direction.

Specifically, geo-registration enables a three dimensional capability to ascertain the size of all moving objects in the field of view for making accurate security determinations. This is particularly important over expansive areas, where small objects close to the camera will appear substantially larger than a person standing off in the distance, as illustrated in Figure 2.

In the following example, a dog at 10 meters from the camera is approximately 250 times larger than a man appears to be at 300 meters.  Cameras that lack geo-registration will interpret that the dog is the larger object and send an alert, while ignoring the person in the distance. 

Depth Perception with Geo-Registration
View larger image
Figure 2: Depth of perception with Geo-registration 

Conversely, surveillance cameras which employ geo-registration will detect human-sized objects anywhere within their field of view, whether near or far from the camera, and ignore small animals, blowing trash or debris.

Perimeter security design and addressing blind spots under the camera

Many outdoor surveillance designs will narrow a security camera's field of view to increase the camera's detection distance in an effort to decrease costs. Doing so will extend the "blind" spot under the camera leaving gaps in coverage that may extend great distances.

The example shown in Figure 3 depicts two perimeter security designs using cameras mounted at twenty feet off the ground with a seven degree horizontal field of view. In the top design, the coverage range of "Camera 1" stops near the base of the pole of "Camera 2", leaving approximately 60 meters of unprotected area where intruders can enter undetected.

Addressing Blind Spots
View larger image
Figure 3: Addressing bling spots 

Determining a camera's true detection range

Understanding a camera's true automated detection range also addresses potential gaps in perimeter security design. This is best accomplished by measuring the camera's detection range when a person walks directly towards the camera, rather than across path of the camera's field of view, as shown in Figure 4.

Figure 4: Determining a Camera's True Detection Range
View larger image
Figure 4: Determining a camera's true detection range

Walking across a camera's view creates a lot of motion, making it easy for the camera to detect the object. On the other hand, when a person moves directly towards a camera, the camera's detection is limited mostly to leg motion, which is a much smaller variation and more difficult to detect at greater distances.

Addressing false alerts from changing lighting conditions

The use of From/To rules can be effective where changing lighting conditions such as headlights cause nuisance alarms. Unlike "trip-wire" rules, which are triggered when a detected object crosses an established line in the camera's field of view, From/To rules use spatial characteristics such as size, speed, bearing and persistence. 

Deploying video analytic systems for outdoor areas and site perimeters can be cost-effective and highly accurate 

From/To rules are invoked when an object that maintains persistent tracking and represents a specific size moves from one region of the scene (the "From" zone) and enters the other area of the scene (the "To" zone). In this case, the camera will determine that the detected object represents a credible threat and send an alert. This is accomplished with the combination of a specific size filter along with a From/To zone to greatly reduce the likelihood of headlights causing a nuisance alert with a visible camera.

The use of thermal imagers represents another viable approach for solving lighting challenges. These include surveillance applications over water, where reflections would cause difficulty for visible cameras, or for surveillance applications that take place in total darkness.

Protective enclosures for outdoor surveillance cameras

Enclosing an indoor surveillance camera in a protective enclosure might protect against some conditions such as rain, but it may not protect against humidity, sand, and extreme temperatures. In the outdoors, normal expansion and contraction occur due to thermal changes throughout the day, allowing grit, dust or humidity to possibly enter the security camera housing and impact the electronics, reducing the service life of the camera.

To address this, choose cameras which have been sealed and nitrogen-pressurized to keep weather from entering into the enclosure, even in extreme conditions. For example, some cameras can operate in environments that range from the Canada Oil Sands to the Middle East due to their sealed enclosures and thermal dynamic packaging to manage heat from the processors.

Containing security costs

The same multi-processing power that gives video analytics-enabled cameras their ability to accurately detect targets often gives them extended range and area coverage - in many cases three times as much distance or area as other cameras. As a result, there is a proportionate project cost savings - typically to the order of 50% - from the elimination of the extra cameras and poles, construction, trenching, power and network connectivity that would have otherwise been required.


Deploying video analytic systems for outdoor areas and site perimeters can be surprisingly cost-effective while also trustworthy, secure and highly accurate. By applying a best practices approach, organizations can meet outdoor security objectives with much higher levels of accountability and cost effectiveness.  

John Romanowich, CEO of SightLogix   John Romanowich
Share with LinkedIn Share with Twitter Share with Facebook Share with Facebook
Download PDF version Download PDF version

In case you missed it

Disruptive Innovation Providing New Opportunities In Smart Cities
Disruptive Innovation Providing New Opportunities In Smart Cities

Growth is accelerating in the smart cities market, which will quadruple in the next four years based on 2020 numbers. Top priorities are resilient energy and infrastructure projects, followed by data-driven public safety and intelligent transportation. Innovation in smart cities will come from the continual maturation of relevant technologies such as artificial intelligence (AI), the Internet of Things (IoT), fifth-generation telecommunications (5G) and edge-to-cloud networking. AI and computer vision (video analytics) are driving challenges in security and safety, in particular, with video management systems (VMSs) capturing video streams and exposing them to various AI analytics. Adoption of disruptive technologies “Cities are entering the critical part of the adoption curve,” said Kasia Hanson, Global Director, Partner Sales, IOT Video, Safe Cities, Intel Corp. “They are beginning to cross the chasm to realize their smart city vision. Cities are taking notice and have new incentives to push harder than before. They are in a better position to innovate.” “Safety and security were already important market drivers responsible for adoption of AI, computer vision and edge computing scenarios,” commented Hanson, in a presentation at the Milestone Integration Platform Symposium (MIPS) 2021. She added: “2020 was an inflection point when technology and the market were ripe for disruption. COVID has accelerated the adoption of disruptive technologies in ways we could not have predicted last year.” Challenges faced by cities Spending in the European Union on public order and safety alone stood at 1.7% of GDP in 2018 Providing wide-ranging services is an expanding need in cities of all sizes. There are currently 33 megacities globally with populations over 10 million. There are also another 4,000 cities with populations over 100,000 inhabitants. Challenges for all cities include improving public health and safety, addressing environmental pressures, enabling mobility, improving quality of life, promoting economic competitiveness, and reducing costs. Spending in the European Union on public order and safety alone stood at 1.7% of GDP in 2018. Other challenges include air quality – 80% of those living in urban areas are exposed to air quality levels that exceed World Health Organization (WHO) limits. Highlighting mobility concerns is an eye-opening statistic from Los Angeles in 2017: Residents spent an average of 102 hours sitting in traffic. Smart technology “The Smart City of Today can enable rich and diverse use cases,” says Hanson. Examples include AI-enabled traffic signals to help reduce air pollution, and machine learning for public safety such as real-time visualization and emergency response. Public safety use cases include smart and connected outdoor lighting, smart buildings, crime prevention, video wearables for field agents, smart kiosks, and detection of noise level, glass breaks, and gunshots. Smart technology will make indoor spaces safer by controlling access to a building with keyless and touchless entry. In the age of COVID, systems can also detect face mask compliance, screen for fever, and ensure physical distancing. 2020 was an inflection point when technology and the smart cities market were ripe for disruption, Kasia Hanson told the MIPS 2021 audience. Video solutions Video workloads will provide core capabilities as entertainment venues reopen after the pandemic. When audiences attend an event at a city stadium, deep learning and AI capabilities analyze customer behaviors to create new routes, pathways, signage and to optimize cleaning operations. Personalized digital experiences will add to the overall entertainment value. In the public safety arena, video enables core capabilities such as protection of people, assets, and property, emergency response, and real-time visualization, and increased situational awareness. Video also provides intelligent incident management, better operational efficiency, and faster information sharing and collaboration. Smart video strategy Intel and Milestone provide video solutions across many use cases, including safety and security Video at the edge is a key element in end-to-end solutions. Transforming data from various point solutions into insights is complicated, time-consuming, and costly. Cities and public venues are looking for hardware, software, and industry expertise to provide the right mix of performance, capabilities, and cost-effectiveness. Intel’s smart video strategy focuses around its OpenVINO toolkit. OpenVINO, which is short for Open Visual Inference and Neural network Optimization, enables customers to build and deploy high-performing computer vision and deep learning inference applications. Intel and Milestone partnership – Video solutions “Our customers are asking for choice and flexibility at the edge, on-premises and in the cloud,” said Hansen in her presentation at the virtual conference. “They want the choice to integrate with large-scale software packages to speed deployment and ensure consistency over time. They need to be able to scale computer vision. Resolutions are increasing alongside growth in sensor installations themselves. They have to be able to accommodate that volume, no matter what causes it to grow.” As partners, Intel and Milestone provide video solutions across many use cases, including safety and security. In effect, the partnership combines Intel’s portfolio of video, computer vision, inferencing, and AI capabilities with Milestone’s video management software and community of analytics partners. Given its complex needs, the smart cities market is particularly inviting for these technologies.

What Are the Physical Security Challenges of Smart Cities?
What Are the Physical Security Challenges of Smart Cities?

The emergence of smart cities provides real-world evidence of the vast capabilities of the Internet of Things (IoT). Urban areas today can deploy a variety of IoT sensors to collect data that is then analyzed to provide insights to drive better decision-making and ultimately to make modern cities more livable. Safety and security are an important aspect of smart cities, and the capabilities that drive smarter cities also enable technologies that make them safer. We asked this week’s Expert Panel Roundtable: What are the physical security challenges of smart cities?

New Markets For AI-Powered Smart Cameras In 2021
New Markets For AI-Powered Smart Cameras In 2021

Organizations faced a number of unforeseen challenges in nearly every business sector throughout 2020 – and continuing into 2021. Until now, businesses have been on the defensive, reacting to the shifting workforce and economic conditions, however, COVID-19 proved to be a catalyst for some to accelerate their long-term technology and digitalization plans. This is now giving decision-makers the chance to take a proactive approach to mitigate current and post-pandemic risks. These long-term technology solutions can be used for today’s new world of social distancing and face mask policies and flexibly repurposed for tomorrow’s renewed focus on efficiency and business optimization. For many, this emphasis on optimization will likely be precipitated by not only the resulting economic impacts of the pandemic but also the growing sophistication and maturity of technologies such as Artificial Intelligence (AI) and Machine Learning (ML), technologies that are coming of age just when they seem to be needed the most.COVID-19 proved to be a catalyst for some to accelerate their long-term technology and digitalization plans Combined with today’s cutting-edge computer vision capabilities, AI and ML have produced smart cameras that have enabled organizations to more easily implement and comply with new health and safety requirements. Smart cameras equipped with AI-enabled intelligent video analytic applications can also be used in a variety of use cases that take into account traditional security applications, as well as business or operational optimization, uses – all on a single camera. As the applications for video analytics become more and more mainstream - providing valuable insights to a variety of industries - 2021 will be a year to explore new areas of use for AI-powered cameras. Optimizing production workflows and product quality in agriculture Surveillance and monitoring technologies are offering value to industries such as agriculture by providing a cost-effective solution for monitoring of crops, business assets and optimizing production processes. As many in the agriculture sector seek to find new technologies to assist in reducing energy usage, as well as reduce the environmental strain of modern farming, they can find an unusual ally in smart surveillance. Some niche farming organizations are already implementing AI solutions to monitor crops for peak production freshness in order to reduce waste and increase product quality.  For users who face environmental threats, such as mold, parasites, or other insects, smart surveillance monitoring can assist in the early identification of these pests and notify proper personnel before damage has occurred. They can also monitor vast amounts of livestock in fields to ensure safety from predators or to identify if an animal is injured. Using video monitoring in the growing environment as well as along the supply chain can also prove valuable to large-scale agriculture production. Applications can track and manage inventory in real-time, improving knowledge of high-demand items and allowing for better supply chain planning, further reducing potential spoilage. Efficient monitoring in manufacturing and logistics New challenges have arisen in the transportation and logistics sector, with the industry experiencing global growth. While security and operational requirements are changing, smart surveillance offers an entirely new way to monitor and control the physical side of logistics, correcting problems that often go undetected by the human eye, but have a significant impact on the overall customer experience. Smart surveillance offers an entirely new way to monitor and control the physical side of logistics, correcting problems that often go undetected by the human eye. Video analytics can assist logistic service providers in successfully delivering the correct product to the right location and customer in its original condition, which normally requires the supply chain to be both secure and ultra-efficient. The latest camera technology and intelligent software algorithms can analyze footage directly on the camera – detecting a damaged package at the loading dock before it is loaded onto a truck for delivery. When shipments come in, smart cameras can also alert drivers of empty loading bays available for offloading or alert facility staff of potential blockages or hazards for incoming and outgoing vehicles that could delay delivery schedules planned down to the minute. For monitoring and detecting specific vehicles, computer vision in combination with video analysis enables security cameras to streamline access control measures with license plate recognition. Smart cameras equipped with this technology can identify incoming and outgoing trucks - ensuring that only authorized vehicles gain access to transfer points or warehouses. Enhance regulatory safety measures in industrial settings  Smart surveillance and AI-enabled applications can be used to ensure compliance with organizational or regulatory safety measures in industrial environments. Object detection apps can identify if employees are wearing proper safety gear, such as facial coverings, hard hats, or lifting belts. Similar to the prevention of break-ins and theft, cameras equipped with behavior detection can help to automatically recognize accidents at an early stage. For example, if a worker falls to the ground or is hit by a falling object, the system recognizes this as unusual behavior and reports it immediately. Going beyond employee safety is the ability to use this technology for vital preventative maintenance on machinery and structures. A camera can identify potential safety hazards, such as a loose cable causing sparks, potential wiring hazards, or even detect defects in raw materials. Other more subtle changes, such as gradual structural shifts/crack or increases in vibrations – ones that would take the human eye months or years to discover – are detectable by smart cameras trained to detect the first signs of mechanical deterioration that could potentially pose a physical safety risk to people or assets. Early recognition of fire and smoke is another use case where industrial decision-makers can find value. Conventional fire alarms are often difficult to properly mount in buildings or outdoor spaces and they require a lot of maintenance. Smart security cameras can be deployed in difficult or hard-to-reach areas. When equipped with fire detection applications, they can trigger notification far earlier than a conventional fire alarm – as well as reduce false alarms by distinguishing between smoke, fog, or other objects that trigger false alarms. By digitizing analog environments, whether a smoke detector or an analog pressure gauge, decision-makers will have access to a wealth of data for analysis that will enable them to optimize highly technical processes along different stages of manufacturing - as well as ensure employee safety and security of industrial assets and resources. Looking forward to the future of smart surveillance With the rise of automation in all three of these markets, from intelligent shelving systems in warehouses to autonomous-driving trucks, object detection for security threats, and the use of AI in monitoring agricultural crops and livestock, the overall demand for computer vision and video analytics will continue to grow. That is why now is the best time for decision-makers across a number of industries to examine their current infrastructure and determine if they are ready to make an investment in a sustainable, multi-use, and long-term security and business optimization solution.