Machines today are being trained to process visual data gathered from cameras to perform many tasks that either aid the people doing those tasks or take over from them altogether. This process is a part of Computer Vision, the technique meant to help computers process visual data like humans.
And the biggest benefactors of this transformation are businesses, which is why they are hiring data labeling companies to aid the creation of such machines. More specifically, these companies are getting hired to generate datasets that can train the software operating those machines, called Artificial Intelligence (AI) models. They get called so because they can learn and adapt to situations, unlike traditional computers that require dedicated programming for each task. AI uses Machine Learning to accomplish this, a method that helps computers learn things by feeding them annotated data. Annotated video data is one form of data used.
So what is an annotation? How is annotated video related to helping computers learn about the world around them exactly? The answers to these questions, and others like them, can be found below.
What Is Data Annotation and Labeling?
Understanding video annotation requires understanding what annotation is in the first place. Consider the example of an old family photograph. You look at it and can identify the persons in it. It gets done by differentiating them by their various features, like face, body structure, etc., that you’ve recollected from memory.
Data annotation does the same for computers. It involves tagging target elements (like the people in the photo) in a data sample (like the family photo) to help the computer identify that target. Data labeling does the same, and the two terms are used interchangeably in most circumstances. The difference is that labeling adds context to the target element and not just boundaries that differentiate it from the background and other objects.
Video annotation is, hence, the annotation process applied to video data samples. It can be considered an offshoot of image annotation since many methods used by the two are the same. Video annotation services provided by professionals use various sophisticated tools to discern the different elements in a video and prepare annotated video data for machine learning algorithms to use for their training and testing.
Why Is Video Annotation Needed?
The world is increasingly automating, with machines getting involved in all levels of the economy and daily life. Businesses are most keen to adopt automation for every possible function due to the many advantages offered. It is little wonder that globally, the industrial automation market is likely to touch US$ 265 billion in 2025, up from US$ 175 Billion in 2020, at a CAGR of 9%.
Another factor driving the need for video annotation is the quantity of video content consumed. Marketing is by and large digital today and video marketing makes up a significant chunk of it, followed by regular TV ads. A survey by Hubspot shows that 66% of consumers have learned about a brand through its video marketing content. It means Video is becoming a significant portion of enterprise data, requiring businesses to focus on video annotation.
Applications Of Video Annotation
- Object Detection: A still object in a video gets annotated in each frame it is present so that the ML algorithm learns to differentiate it from the background and other objects. It helps identify objects relatively stationary to the elements of the frame surrounding them, the background, or both.
- Motion Detection and Tracking: An object’s movement in the video is detected and tracked by comparing its position in one frame relative to the other elements in it to the same in the next frame using annotation. It helps detect the movement of the target and other characteristics like speed.
- Color Identification: Annotation is used to demarcate the portion of the video and label its color. It is useful in detecting changes in the color of a target object or a portion of the video.
Use Cases and Benefits Of Video Annotation
A video is a versatile form of data, finding use in many applications across industries. And video annotation is also finding use in all those applications.
Security and Safety
An example of technology-based safety and security measures is the CCTV system. Cameras record videos of a location continuously, with a security person, an AI, or both, monitoring it for any suspicious activity. The AI does this by tracking the movements of people and objects in the video frames and extrapolating the intent of the target person from them.
Data labeling companies train such a system by annotating the subjects’ faces, bodies, and objects of thousands of sample targets using many hours of video data. In the end, they can analyze the real-time video from cameras and perform predictive analysis on suspected target subjects by tracking their movements and comparing them to known negative behavior.
The electric car maker Tesla uses such technology in its Sentry Mode car safety feature to automatically record video of people damaging or trying to steal the car. Cameras on the car’s outer surface start recording videos of strangers that demonstrate behavior that may indicate that they are resorting to such deleterious activity. It helps save the limited onboard data storage while recording the suspect’s identity.
The automotive industry is pushing towards adopting autonomous vehicles, otherwise called self-driving vehicles. They are said to offer more safety and convenience to users since it eliminates human error, which is the leading cause of accidents. Video annotation services help accomplish this feat by labeling the various elements present in the real-time on-road video data obtained by the vehicle’s onboard AI through its cameras.
The AI gets trained using video annotation to determine the type of object it is looking at (animate or inanimate), its position relative to the vehicle, its various characteristics (color, shape, size, height, etc.), and its speed relative to the vehicle. The aim is to have driverless vehicles that can move about fully autonomously, relieving the stress and other issues driving brings to people. It is also being extended to other forms of transport like airplanes, naval vessels, drones, etc.
Robots have long been a staple of the manufacturing sector, and the trend looks set to continue, with the intent to automate the process. This goal requires that the AI operating the various machines and tools on the factory floor and elsewhere be fully aware of what it is using, its condition, position, and other applicable characteristics. It should analyze real-time data from various sensors to have such situational awareness, the most important of which is visual data.
Professional data labeling services providers can help create such AI by annotating the video feed from the various cameras used. The AI can be improved to such an extent that it can identify flaws in the finished product as it rapidly moves on the conveyor, saving time and costs while ensuring high output quality and quantity. Worker safety improves by using AI to monitor their movements, besides their productivity.
Logistics and Supply Chain
In this globalized world, supply chains are complex, connecting multiple warehouses and markets with various regulations. The logistics industry also uses many vehicles at once spread all over the world in many cases. Video-based tracking of these vehicles and their packages, along with other functions like documentation, is necessary for the smooth functioning of the entire chain.
These functions can be automated by the use of AI trained using video annotation. It assists in identifying the many components of the supply chain. It can determine the position, speed, ETA, and other crucial aspects of vehicles and manage inventories across warehouses through functions like dynamic barcode scanning.
Automation is working wonders for the healthcare industry. Surgeons can now perform increasingly complex procedures on patients remotely using robotic arms and AI. Radiologists have AI as a helpful companion in detecting health issues in various scan results.
Many more of AI’s benefits are transforming healthcare, and video annotation is behind it all. It helps the AI learn the difference between a healthy body part and an unhealthy one, identify the organ it’s viewing while performing remote surgery, and many other functions. Thus, it’s helping save costs while also saving lives.
Video Annotation Best Practices
Here are the actions you can apply to get the best video annotation results for your AI/ML development.
- Ascertain the objectives of the AI model and plan your operations accordingly. Budget to get the most value and assign respective persons for the tasks.
- Choose your data sources and have the IT infrastructure to support the applicable data management.
- Outsource the process to a dedicated video annotation services agency. It helps you save costs and other vital resources. Select the agency based on its history, delivery time, prices, expertise, staff strength, etc.
- Maintain good communications with the outsourcing agency, including scheduling regular meetings and discussing pertinent issues.
- Always keep your data clean and of high quality. Poor quality data is a deterrent to smooth annotation.
- Periodically review the AI/ML model and the training methodology to adapt them to changing business requirements. Repeat it to incorporate new techniques and technologies and maintain the market edge.
As video continues to grow its relevance in all spheres of business and life along with automation, it becomes increasingly inevitable to opt for video annotation. The intelligence annotation helps develop for your business AI is incomparable to any other measure you can take to utilize your video data. Going with an image annotation services agency for the purpose only improves the returns you’ll get for the endeavor, especially if you follow the best practices mentioned. Your business’s proposition value will skyrocket while your efforts become less taxing on you and your resources.
Read more: Effective Ways to Approach Quality Software Development