The annotation of videos works the same way as an image annotation. However, it’s more complicated and challenging since you are dealing with moving objects. These objects in the video are labeled for machine learning and used in different industries, including the following.
There are now retail stores that allow members to get something and automatically charge them without paying at the cashier. Customers can create an account to be a member of the retail store, have their picture taken, and supply their payment details. Cameras are positioned in different store areas, and they detect the customers and the items they purchased. As they leave the store, the system will recognize their face and charge them on their account. Both video and image annotations are used for the accuracy of this process.
One of the industries known to use machine learning and AI is the automotive industry. Autonomous cars are no longer a thing of the future. Manufacturers are now offering these vehicles, although they still require further development to achieve full automation. However, there are now several features that can help assist in safe and convenient driving without the complete control of the driver.
Some of them are sensors that prevent the car from bumping or hitting something or someone. It can also detect stop signs to know when to stop and other objects around to prevent accidents. Video annotation is used to make the program learn about these objects and determine the best course of action to take. As more data is fed, the machine learns more and increases its accuracy.
Annotation is also used in the medical field. For example, AI may be used to direct a surgical tool during surgery. Several videos of that surgical procedure may be annotated for machine learning. As a result, the program learns more about the process as it gets more data.
Challenges in annotating videos
- Massive amount of data. The more data the machine has, the more accurate it works. There is usually a vast volume of data involved in annotating videos. They must be annotated frame by frame, which means long hours of work if done manually. There are now tools that can help make annotation quicker.
- Annotation accuracy. The accuracy of the annotation is vital for effective machine learning. Since there is much work involved in annotating videos, the annotator can get tired and lose focus, affecting data accuracy. If the labels are inaccurate, they will not serve their purpose.
- Moving objects. Since you are dealing with moving objects, annotating them will be more challenging than images. You have to annotate frame by frame, and that can be tiresome.
With all the challenges you may face in annotating videos for machine learning or AI, there is the option to outsource the task to specialists in this field. They have the tools and experienced and trained annotators to do the job for you. You can leave the hard work to them, and they can give you accurate results, as long you deal with a reliable service provider.