We live in a world that is driven by data. In order to stay ahead of their rivals, many businesses have started utilizing this data to enhance decision-making and business efficiency. One of the primary technologies that are used by these businesses is ai-powered real-time video analytics. In this article, we will discuss how you can leverage real-time video analytics for growing your business.
With the advances in the world of artificial intelligence, video analytics has enabled businesses to automate tasks that were previously under the purview of humans. Today, we can see video analytics being used in various areas of our everyday lives, from the face recognition technology on our phones to the detection of abnormal events in public spaces. However, is it possible to use this technology to improve your business functioning? In order to ascertain how real-time video analytics can help your business, let us first understand the below topics in detail.
Real-time video analytics has become a technical buzzword in the business world. As companies have started aligning themselves with emerging technologies like artificial intelligence, we can see terms like video analytics and computer vision gain popularity. Regular CCTV cameras have become an integral part of any business. They help companies monitor their buildings and other physical assets. However, it can be extremely challenging for security personnel to monitor each and every camera at all times, which leads to low levels of situational awareness. Moreover, these cameras produce large quantities of footage, which makes it difficult for security teams to manually review it during post-incident investigations.
Real-time video analytics can help combat these problems by processing video in real-time, identifying objects in the video footage (people, vehicles, and other items), and automatically indexing them so that the footage can be searched and analyzed with ease. Real-time video analytics tools can perform real-time surveillance of various events. They may capture and analyze objects, object attributes, movement patterns, or behavior. Some of the common events that such solutions are used to detect include identifying vehicles that are not following traffic rules, detecting people who aren’t wearing helmets or suspicious activity in the business premises. Since the COVID-19 pandemic, many businesses are also using such solutions to identify people who aren’t wearing masks or personal protective equipment.
Traditionally, video analysis is conducted by security personnel. They observe the video footage and draw conclusions manually. Here, humans perform the function of observing data, making notes when required, taking action when abnormal activities arise, watching out for unusual events, and more. However, their ability to identify abnormal events quickly and accurately is limited. Some of the main issues that businesses face with manual surveillance of footage include-
The attention span in humans is much less when compared to the zero error workings of AI-powered video analytics. Humans can only hold an attention span of 8 seconds, which can lead to inaccurate and slow identification of abnormal events or the inability to recollect previous happenings. On the other hand, a video analytics solution will never face the issue of attention span.
We all know that if a person keeps working for a continuous period, their energy levels will be compromised. They will often experience fatigue, tiredness, and ultimately low productivity. New age video analytics-enabled with AI is capable of performing consistently without any compromise in productivity levels.
Humans are prone to bias even though they think they are rationally analyzing things. This often has an impact on the way humans analyze and develop reports from video footage. A small bias can make the entire report inaccurate. Whereas, machines just perform what they are told to perform. Hence this leads to reports and data that are free from bias, and it can prove to be very helpful in making business decisions.
Business industries are quick to realize the growth and benefits they can gain from the use of AI-enabled video analytics solutions. Some of the ways in which we can see such tools being used include:
According to MarketsandMarkets, the global video analytics market is expected to grow from USD 4.9 billion in 2020 to USD 11.7 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 19.0% during the forecast period. The expected growth rate of the video analytics market globally is at a CAGR( Compound annual growth rate) of 19.0% during the forecasted period, from2020 to 2025. The video analytics market is expected to grow from USD 4.9 billion in 2020 to USD 11.7 billion by 2025.
The factors responsible for its growth include:
Real-time video analytics solutions analyze data from various streaming video sources. The most commonly used sources that we see are CCTV cameras, traffic cameras and online video feeds. The solutions can be run centrally on servers, which is known as central processing. They can also be embedded in the cameras themselves, which is called edge processing. Let us take a closer look at these types.
Once you have finalized the physical architecture for the solution, you will have to define the situations on which you want to focus and then train the models that are going to detect the target events. Do you want to count the number of in-store customers or detect the number plates of vehicles in your parking space? Each situation will involve a series of basic tasks that the system must know how to perform.
The most frequent, basic tasks in video analytics are:
To know more about the basic tasks performed and the types of algorithms that are used to develop video analysis software, we recommend you read this introductory guide to computer vision.
Video analytics algorithms are generally based on pattern recognition. We train computers on large volumes of visual data—computers process images, label objects on them, and find patterns in those objects. For instance, if we take a thousand photos of cars, the computer vision algorithms will analyze them, identify patterns that are similar to all the other cars and then create a model “car.” As a result, the computer will be able to accurately detect whether a particular image is a car every time it encounters a photo. Let us take a closer look at how such solutions perform this task.
During the first step, the computer converts the image or a video into an array of pixels and then identifies the various attributes of an object. Most of the time, deep learning algorithms are used to automate this process.
After this, the computer needs to be trained so that the video analytics solution can be applied. During training, thousands of images are fed to the system, so that system can learn the difference between different objects and differentiate them. Once the machine analyzes the pixels, computer vision algorithms use a neural network to predict the content of the image. With each prediction, the computer is fed through the different layers of the neural network many times so that the machine comes with a correct prediction. With multiple processing, the machine comes up with correct predictions in the form of probability.
Training models from scratch can be an extremely complex process. Most businesses do not have the technical knowledge required to do this themselves. This is why it can be extremely beneficial to partner with an AI development company at this point in time.
Some of the prominent technologies that are used during the development of video analytics solutions include:
The Open Source Computer Vision Library (OpenCV) is one of the most popular computer vision libraries. It contains a wide array of machine learning algorithms to execute basic tasks like image classification, face recognition, and object detection and tracking. It is widely used by companies and research groups, as it can be used via its native C++ interface, or through Java and Python wrappers.
Luminoth is an open-source toolkit for computer vision. It is built in Python and based on TensorFlow and Sonnet. This library was developed by Tryolabs. It helps computers identify objects in a photograph with pre-trained checkpoints.
This library focuses on object detection, segmentation and pose estimation. The advantage of Detectron is its ability to not only draw bounding boxes around individual objects but to create pixel-level masks, which define the boundaries of an object. Detectron is ideal for research and to train custom computer vision models.
As you can see, there are many benefits associated with adopting AI-powered video analytics solutions. Businesses can use these tools in a variety of ways to expand their operations and improve productivity. Some of the main applications include:
Video analytics solutions can analyze video footage and detect any kind of security threat in real-time. They can also send real-time notifications and alerts to the security team so that the appropriate course of action can be taken. Additionally, many businesses use these solutions to block suspicious people from entering a certain area.
AI-enabled video analytics can monitor the movements in a store and provide multiple benefits that ordinary CCTV surveillance cameras do not. AI video analytics can provide data such as the total time a customer spends in the shop, which products cater to his interest, and which do not. It is also possible to understand the emotions of customers while looking at a particular product. Such solutions can also generate real-time heat maps, track customer journeys, count the number of footfalls in the store and provide other in-depth insights that can help businesses improve decision-making abilities.
AI analytics can help businesses craft smarter strategies by analyzing data from video footage and providing insights based on this data. Businesses can use these insights to improve their product placement, queue management abilities, resource allocation and more. These tools can also monitor the movement of the crowd in the premises and display data such as the total number of people present, arrived, left, total employees present in the venue, region of high and low traffic, occupancy details of halls, booths, etc. This allows the management to make data-driven decisions for the arrangements of facilities. ADNEC, one of our clients, was facing severe losses due to mishandled activities. We helped them out by developing a video analytics solution that improved their facility management abilities. To get a better idea about how the solution helped them, you can read more over here.
AI-based video analytics can help to increase the safety of the workers in factories. Such tools can be used to track whether workers are wearing their safety equipment or if there is overcrowding in a particular area. They can also be useful in flame or smoke detection, short circuit detection, unknown visitor capture at prohibited work areas etc.
In manufacturing units, video analytics solutions can identify and analyze critical supply chain movements. On-site drone videos and cameras can also be used to monitor critical assets or threats to workers, equipment, vehicle, and locations.
One of the main reasons for the increased demand for AI-powered video analytics is because it solves many problems that humans cannot solve. Businesses have found that AI-empowered video analytics can help to monitor their premises and provide accurate results. We can eliminate human errors with AI-enabled video analytics and surveillance. Investing in an AI-enabled video analytic device is both promising and worth every penny for the organization. It contributes to organizational growth in the long term.
Talk to our experts today