You’ve probably seen videos with annotations, such as bounding boxes, arrows, texts, and other shapes. These elements enrich the overall video experience by adding information and, as a result, increasing engagement and interactivity.
In the development of artificial intelligence, however, video annotation represents something else entirely. In this discipline, annotations added to videos are enabling those at the cutting-edge to give machines the ability to ‘see’, and to interpret the world via visual data.
The centrality video annotation holds for computer vision is plain to see, but how far can it really go, and why does it represent such a specialised area of AI? Read more below.
Artificial intelligence is essential today for a wide range of applications. AI can automate complex tasks, scour reams of data that would otherwise strike fear in the hearts of the most avid researchers, and introduce insights that can change the playing field in an instant.
Computer vision represents to a branch of study within AI development concerned with training machines to understand and interpret visual data (images and video) and extract meaning from those stimuli. Even off that brief description alone, it is clear that computer vision has the potential to transform industries – and life as we know it. Perfecting this technology enables machines interpret the world in new and exciting ways. You can use visual data to a strategic advantage for training neural networks, from smartphone apps that can identify plants or animals, precision farming, to non-contact food delivery.
But, in computer vision, the challenges and the opportunities are equal in number. In order to develop computer vision models, you must have access to a large amount of available data – data that is labelled or annotated carefully so they can be useful in supervised machine learning.
Video annotation is field within image annotation. It uses the same techniques and tools of image annotation, although the process of video annotation is more complex. The complexity lies in the number of frames per second. This means that annotating videos takes more time than annotating images, and needs more advanced features from the annotating tool.
Video annotation requires the addition of tags to unlabelled video to train a machine learning algorithm. It can train algorithms for various tasks, including the classification of objects, or tracking their movements across several frames.
There are so many use cases for video annotation. It can be used to train autonomous vehicle systems for street boundary recognition. Medical AIs can use video annotation for surgical assistance and disease identification. It is being used to develop checkout-free retail situations, where customers are charged according to what products they take from a store. You can foster learning and professional development with it.
Video annotation captures the main object frame-by-frame. It makes key object recognizable to machines. It helps localize the main object if there are multiple objects in the video. The process can teach machines to track human activities and estimate their poses, such as in sports. In autonomous flying drones and self-driving cars, video animation helps train the model for localization, recognition and accurate detection of various objects.
As you can see, video annotation is becoming more essential for various industries. As such, it is vital to choose a platform that will make the process of preparing your visual data faster and more streamlined.
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