Over the last few years, industry and academia have become more interested in video analytics, also referred to as intelligent video analytics or video content analysis. Tasks that were previously only possible by humans can now be automated due to the widespread use of deep learning in video analytics.
Recent advancements in this field have revolutionized a variety of applications, from those that track traffic congestion and provide real-time alerts to those that evaluate consumer flow in stores to optimize sales, in addition to more well-known applications like facial recognition and smart parking.
How does this technology operate, and how can it help your business? It looks great to reinvent security and operational insight.
The fundamentals of video analytics and how it is transforming security and operational insight for CIOs are covered in this article.
Let’s dig in!
What is Video Analytics?
Video analytics is a cutting-edge technology that automatically examines video footage. Real-time processing of video data by intelligent algorithms produces insights about the events depicted in a sequence of photos. Detecting and learning about the movement of objects, people, and vehicles in CCTV data is a popular application of video analytics for security.
A better and more efficient way to review and view security footage is through video analytics surveillance systems. Multiple cameras’ worth of footage over several days can be auto-filtered by topics of interest, and security staff can spot and react to suspicious activity.
How Does Video Analytics Work?
Here is a broad overview of how a video analytics solution operates. The scheme is always the same; however, the architecture of a solution might change depending on the specific use case.
There are two methods for analyzing video content: either in real time, by setting up the system to send out alerts for particular incidents and events that happen right then, or in post-processing, by carrying out sophisticated searches to make forensic analysis chores easier.
Feeding the System
Several streaming video sources may provide the data for analysis. Online video streams, traffic cameras, and CCTV cameras are the most popular. Nonetheless, the solution can typically incorporate any video source that employs the relevant protocol (such as HTTP or RTSP, or real-time streaming protocol).
Coverage is a crucial objective; one must be able to see the entire region and the potential locations of the events under observation from a variety of perspectives. Keep in mind that, as long as it can be handled, more data is preferable.
Edge Processing vs Central Processing
Central processing refers to the ability to run video analysis software centrally on servers that are often found in the monitoring station. Another option is to incorporate it within the cameras themselves, which is referred to as edge processing.
When building a solution, the camera selection should be considered. Many old software programs were written with central processing power only. But hybrid systems are becoming more common these days. However, it’s always good to focus on forensic analysis on the central server and real-time processing on cameras whenever possible.
By using a hybrid approach, the cameras process less data for the central servers to handle, which would normally require a lot of processing power and bandwidth as the number of cameras increases. And the software can be set up to only send information about suspicious events to the server through the network, which reduces network traffic and storage requirements.
In the meantime, centralizing the data for forensic analysis makes it possible to apply a variety of search and analysis methods, ranging from ad-hoc implementations to generic algorithms, each of which uses a unique set of parameters to assist in balancing the noise and silence in the findings. In essence, you can use your own algorithms to achieve the intended outcomes, making this a particularly adaptable and alluring plan.
Establishing Training Models and Scenarios
Following the planning and installation of the physical architecture, you must specify the scenarios you wish to concentrate on and train the models that will identify the desired occurrences.
Car crashes? Flow of the crowd? Can a retail establishment use facial recognition to identify known shoplifters? Every situation results in a set of fundamental tasks that the system has to be able to complete.
As an illustration, identify vehicles, eventually identify their type (e.g., truck, car, or motorbike), follow their route frame by frame, and then examine how those pathways change over time to identify potential collisions.
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Why CIOs Are Using Video Analytics for Better Insights
Video analytics is changing security and operational insight for CIOs by turning footage into intelligence. Advanced AI-driven tools now go beyond monitoring to detect anomalies, track behavior patterns, and predict risks in real time. This means faster threat response and valuable insight into operations, workforce efficiency, customer behavior, and space utilization. For CIOs, it means using existing video infrastructure as a strategic asset, safer and better decision making without extra cost.
At the same time, video analytics is helping CIOs align technology with business goals. By integrating video data with enterprise systems, organizations can optimize resource allocation, improve compliance, and strengthen operational resilience. From reducing downtime in industrial environments to enhancing customer experience in retail, these insights allow CIOs to measure outcomes, making video analytics a key enabler of security and digital transformation.
Industry Applications
Smart Cities / Transportation
The development of smart cities has benefited greatly from the application of video analytics in the transportation sector.
If proper traffic management measures are not implemented, a rise in traffic, particularly in urban areas, may lead to an increase in accidents and traffic congestion. In this situation, intelligent video analysis tools can be quite helpful. Traffic is a big problem in urban areas, wasting time, money, and lives. According to the U.S. Government Accountability Office, congestion is going to get worse, so we need intelligent transportation systems that use video analytics to monitor and manage traffic.
Traffic analysis can be used to track traffic congestion and make dynamic adjustments to traffic signal control systems. Real-time detection of risky situations, including a car stopped in an unlawful area on the highway, a driver traveling in the wrong direction, a car moving strangely, or a car that has been in an accident, can also be helpful. For example, the U.S. Department of Transportation deployed AI-powered Adaptive Signal Control Technology in 8 Florida cities and reduced travel time by 9.36% across several corridors. These systems are useful for gathering evidence for a lawsuit in the event of an accident.
The city of New York provides an excellent illustration of how video analytics can be applied to tackle practical issues. The New York City Department of Transportation employed machine learning and video analytics to identify parking infractions, traffic congestion, weather trends, and other significant traffic incidents. The cameras record the events, analyze them, and notify city officials in real time.
Security
Video surveillance is an old task in the security domain. However, from the time that systems were monitored exclusively by humans to current solutions based on video analytics, much water has passed under the bridge.
Facial and license plate recognition (LPR) techniques can be used to identify people and vehicles in real-time and make appropriate decisions. For instance, it’s possible to search for a suspect both in real-time and in stored video footage, or to recognize authorized personnel and grant access to a secured facility.
Another essential security system function is crowd management. In locations like malls, hospitals, stadiums, and airports, advanced video analysis systems can have a significant impact. When a threshold is achieved or exceeded, these systems can send out notifications and provide an estimated crowd count in real time. In order to identify movement in undesirable or forbidden directions, they can also examine the flow of the crowd.
One of the main benefits of these methods is that video content analysis systems may be trained to identify particular occurrences, often with a high level of complexity. Identifying flames as soon as feasible is one example. Or, in the case of airports, to sound an alert if someone walks in the wrong direction or enters a prohibited area. The real-time detection of unsecured luggage in a public area is another excellent use case.
Algorithms that can filter out motion from wind, rain, snow, or animals allow for the robust execution of traditional tasks like intruder detection.
In the field of security, the capabilities provided by intelligent video analysis are expanding daily, and this trend is expected to continue.
Concluding Thoughts
Video analytics tools are incredibly helpful in our day-to-day activities. This technology can be applied to a wide range of industries, particularly as the complexity of possible uses has increased recently.
From smart cities to airport and hospital security measures to retail and shopping center personnel tracking, the field of video analytics makes operations more efficient and less time-consuming for people while also saving financial resources for companies.