Video and image annotations are processes that need to be carried out when creating computer vision models. They involve labeling videos and images of datasets in an attempt to train a machine learning model. When carrying out these processes, you should provide image and video-specific information to computer vision models. All this should be done to make the model recognize and predict the predetermined features contained in images and videos that have not been annotated. Below are more details on video and image annotation.
Table of Contents
Image annotation involves labeling different images. That helps a computer vision model know the essential parts of the annotated image, popularly known as classes. The information helps the model point out the classes in images that have not been annotated.
On the other hand, video annotation involves tagging or labeling different video clips used to train machine learning models. This type of annotation uses the same concept as an image annotation. It is used across many industries for different applications such as medical AI, geospatial technology, and self-driving cars.
These are all possible through the help of training data. What is training data? Machine learns algorithms through data, according to Appen, “They find relationships, develop understanding, make decisions, and evaluate their confidence from the training data they’re given. And the better the training data is, the better the model performs.”
Machine learning has brought a lot of change in the business world. Through artificial intelligence, machines can behave and think like human beings. That is usually made possible through video and image annotation.
With the help of these two processes, objects of interest can be detected easily. For instance, think about a scenario where security guards are trying to prevent unauthorized entry into restricted areas. If they do this manually, they are likely to take time and probably make some errors. However, if you use a security camera with some machine learning algorithm, the camera will easily detect people who are not allowed into the restricted area. It will less likely make errors when differentiating people. Below are other benefits of video and image annotation.
You can use annotated images and videos to track objects or even people. For instance, with the help of this technology, you can track all the actions performed by a particular individual. That can be helpful to you if you are a professional athlete coach. With the help of data annotation services, you can track all the movements that professional athlete makes and analyze how these actions influence how they run. Such information can help you train your athletes to run better and win in competitions. However, you should ensure that you work with a company that uses automation tools when annotating videos.
Think about a situation whereby you are trying to find someone who got lost. The process can be tiresome and probably unsuccessful if you decide to carry it out manually. However, with the help of video and image annotation, you can find that person easily.
In such a case, you should think about any unique features that are on the face of the lost individual and then feed this information into a program. You should then check all the security cameras in your area to check if the person had been captured anywhere. However, you should ensure that your annotation work is correct. Otherwise, you will end up identifying the wrong person.
If you own an e-commerce business, you know how important product listings are. Video and image annotation can help you improve these listings, helping customers find the right products. That is usually beneficial to customers who search for items by uploading pictures in the search bar. In such a case, your computer will use artificial intelligence to find the objects that match the uploaded pictures. However, that will only be possible if the components of the uploaded photos match the details of the annotated images.
Video and image annotations are used in many applications, including traffic signals, flying drones, and robotics. They are processes that you need to carry out when creating computer vision models. They are quite beneficial, and you should make good use of them if you intend to make your organization more tech-savvy.