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What Is the Use and Purpose of Video Annotation?

We know that videos have now become a part of our lives, as videos can be watched by anyone from anywhere, at any time. We can easily share them with our friends by uploading them on YouTube or on viral videos sharing sites. As there are many videos available, video annotation has become important. There are many uses of video annotation. It is used by many industries. iMerit provides this service to many companies and industries for developing AI and ML technology. The first and probably still most significant use and purpose of video annotation was the capture of the object of focus frame-by-frames and making it easily recognizable to computers. The object location is annotated on a grid which is the basis of object recognition. The more the user places in front of an object, the better the results from machine learning will be. The second use and purpose of video annotation have to do with the creation of the content of the videos. As I mentioned above, the moving objects also run on the virtual screen, thus the need to align them with the visible frame. So when the user tries to navigate the video, he needs to be able to easily identify which frame is the part of the video where the action or target can be found. But the human eye is not always capable of this. And so this problem was solved by the use of video analysis software like the ImageNet Eye Recognizer, which can automatically detect the object locations. The final use and purpose of video analysis and annotating come when the system already allows the user to specify the properties of the objects. This was done by the use of a computer vision tool called the Deep Learning Tool. It has been specifically designed for the use and purpose of video annotation in the Fuzzy Logic environment. In the past, the use of a Deep Learning Tool for the alignment of moving objects was possible only for supervised training. But the recent developments in the Deep Learning Tool technology allow the users to use and apply the feature to unsupervised training as well. The main idea behind the usage of this tool in the context of self-driving cars was to enable the car to adjust its path in the face of any change in visual perception. Now, this idea has become a reality with the use of the Deep Learning Tool.