Video Analytics: What Does it Mean and How Does it Actually Work?
Last Updated on October 1, 2021 by Team Wobot
Over the past few years, video analytics has gained interest from various industries and businesses worldwide. Video analytics, also known as video content analytics, helps automate tasks that were previously entirely dependent on humans. As a result, it leaves a lot of room for businesses to employ their workforce in other crucial jobs, which helps improve productivity and the overall operations of a business. Along with this, video analytics can also support organizations in keeping track of their hygiene, safety, and security.
We now understand what video analytics can do, but how does it actually work and benefit your businesses?
In this blog, you will discover the basic concepts of video analytics, how it works, and how it is used in the real world.
What is video analytics?
Video analytics refers to using Artificial Intelligence to analyze real-time videos to detect anomalies according to pre-fed data. This technology detects and tracks objects, activities, and people and helps in improving day-to-day operations. In addition, it analyses historical and real-time footage to learn from mistakes and applies it to develop solutions and make essential decisions beyond human capabilities.
How does video analytics work?
You might have got an idea of what video analytics means. But how does it work and produce desired results?
- Feeding the system
There is a saying, “You are what you consume.” It’s perfectly accurate in the case of AI. The quality of the decision made by Artificial Intelligence is as good as the data it is fed. No matter how advanced the model is, the decision would be substandard if its data is not good. So, feeding the system with the right and extensive historical data will help the AI be in its prime while making important decisions. It is necessary to provide a considerable amount of real-time images, videos and recorded footage to the video analytics software to accurately analyze a video and come up with a decision.
Relevant data comes mainly from CCTV cameras. First, there must have a clear view of the entire territory from different angles. This step enables the software to capture the same visual event from a different perspective so the analysis could be accurate. Gathering more data is good if the system can process it efficiently.
- Cloud Computing vs. Edge Computing
In a world where data is precious than oil, a large volume of data is captured every passing second. Hence, it needs to be processed for its analysis to happen. There are two modern technologies for this process:
- Cloud Computing
Cloud computing is the availability of computer system resources remotely and on-demand without direct active management by a user. As the name suggests, enormous amounts of data are stored in servers, in a cloud or virtual space, instead of hard disks or proprietary local disks. This data can be accessed remotely from anywhere in the world through the internet. Furthermore, once you connect to the web, you can access large amounts of data without being present nearby the database. This lets you access your required data on demand from the comfort of your home.
Cloud computing technology aims to make users capable of using cloud storage without deep knowledge about them. It aims at cost-cutting and lets users focus on core business instead of hindrance by IT drudgery. It mainly works on virtualization technology, separating a computing device into different virtual devices to efficiently manage and perform complex tasks. Virtualization enables the users to speed up their IT operations efficiently and at a low cost.
- Edge Computing
It’s a paradigm involving a distributed network of computers whose components are located on different computer networks which operate on the same communication protocols by passing messages to one another. It brings data storage and computation closer to the work area to improve response time, latency, and bandwidth. Its main applications lie in “instant data” or real-time data processing where all work is outside the cloud.
Edge computing aims to move computation to the edge of networks, far from data centers, utilizing smart objects and network gateways to provide better services and perform tasks efficiently on behalf of the cloud. By moving computation to the edge, it’s easier to dispense content caching, persistent data storage, and better IoT management, which results in better transfer rates and response times.
Video analytics software can either run on cloud servers known as central processing or implanted in cameras themselves, called edge processing. While both processes are good, a cloud solution is preferable for processing real-time camera feeds and complex analytic functionalities for non-critical tasks. In addition, in cloud-based video analytics, there is less upfront investment on hardware, is easy to deploy, and has zero infrastructure cost.
Furthermore, using cloud technology, we can now configure the software to send only actionable data to the servers to reduce network traffic and more storage requirements.
- Cloud Computing
- Defining scenarios and training models
Once your physical architecture is set up, we must define the relevant scenarios we want our software to focus on and then train our models to detect and track target events.
Let’s take an example of a manufacturing company and how the hardhat, which is commonly used on the site, is recognized with the help of video intelligence.
- Image Classification
In image classification, the technology identifies what are easily recognizable images or objects using unique colors, pattern, and format. In our example, hardhat can be easily recognized while monitoring operations. This process is known as image classification in layman’s terms.
Now let’s take an example of hardhat placing along with the safety jacket of the same color. Now there are multiple objects and the technology could find challenges to identify it. That’s where localization comes to rescue. It trains the camera to differentiate between multiple objects and provides correct results.
- Object Detection
However, to attempt localization there needs to be some training involved. That’s where object detection is helpful. It trains the algorithm in a way that it can differentiate between multiple objects and helps us give the right results by identifying key differentiators.
We also need to train our models from scratch, which requires a tremendous amount of effort. But we have some resources available which make this a less tedious task. For example, image datasets such as ImageNet or Microsoft Common Objects in Context (COCO) play a crucial role while training new models. Recently, open-source projects are being published which deal with building a custom video analysis system.
- Image Classification
- Human Review
Finally, a human is needed to review all the alerts sent by the video analytics software and act upon them. With the help of such advanced systems, operators can now detect main events which may be overlooked or would take several hours to see manually.
Many sectors like manufacturing, retail, food services, hospitality, drive-thrus, and QSRs can benefit from this technology. Let us learn how.
- QSRs and Drive-thrus: Drive-thrus can use video analytics to count vehicles, study the wait-time of the vehicles, and also for automatic number plate recognition (ANPR) based on customer identification.
- Hospitality: As guest experience is the driving force behind the hospitality industry, video analytics can help assure guests have the best experience by ensuring concierge availability, clean surroundings, and secure premises.
- Food Services: Restaurants can benefit significantly from AI-powered video analytics by automating the monitoring of various hygiene, cleanliness, and safety practices such as PPE usage, mopping, handwashing, and many more.
- Retail: Video analytics can help retailers understand the traffic areas in their store, manage queue length and footfall.
- Manufacturing: From use cases ranging from accident safety to safety gear to assembly line productivity, manufacturers can use intelligent video analytics to improve workplace safety and productivity.
With intelligent video analytics, we can perform tasks more effectively and less tediously, which is also less expensive. Organizations can leverage it to automate tedious and monotonous processes, gain valuable insights and make better business decisions.
Wobot.ai is a Video Analytics platform equipped with 100+ AI-powered checklists. These checklists span across industries such as QSRs, Drive-thrus, Cloud Kitchens, Restaurants, Hotels, Retail, and Manufacturing. In addition, the platform is compatible with all types of CCTV cameras and supports quick viewing, multi-device access, and robust remote assistance. With Wobot.ai, businesses can gain continuous feedback on processes, focus on areas of improvement, and highlight role models within organizations.
To use Wobot’s Video Analytics for your business, visit https://app.wobot.ai/signup.