Video on other hand provides continuity across frames, limiting the possibility of errors. This can be difficult and prone to error. While labeling images, it is essential to use the same annotations for the same object throughout the dataset. This process can be made quicker by automating it. While labeling one must synchronize and keep track of objects in various states between frames. Annotation processĬomparing video annotation to image annotation, there is an additional level of difficulty. In an image, this information would be lost. Video data can also use data from previous frames to locate an object that might be partially obscured or contains occlusion. Annotation tools allow you to add this extra information to your dataset to be used for training ML models. A video on the other hand would provide not only the direction but provide information to estimate its speed compared to other objects in the image. Let’s discuss the three major aspects: DataĬompared to images, video has a more intricate data structure which is also the reason it can provide more information per unit of data.įor example, the image shown doesn’t provide any information on the direction of movement of the vehicles. But there are considerable differences as well between the two. Video annotation tools help manage these large datasets while ensuring high accuracy and consistency in the process of labeling.Īs one might think, video and image annotation are similar in many aspects. Labels can be used for everything from simple object detection to identifying complex actions and emotions. This can be carried out manually or, in some cases with AI-assisted video labeling. In order to train computer vision AI models, video data is annotated with labels or masks. Video Labeling for Computer Vision Models We’ll then look at video annotation tools and discuss best practices to improve video annotation for your computer vision projects. Then we’ll look at the fundamental elements of video annotation and how to annotate a video. In this guide, we’ll start with understanding video annotation, its advantages and use cases. Video data labeling on the other hand is an entirely different beast! It has an added layer of complexity but you can extract more information from it if you know what you are doing and use the right tools. Hence, video annotation plays a crucial part in training computer vision models.Īnnotating images is a relatively simple and straightforward process. Computer vision has numerous cool applications like self-driving cars, pose estimation and many others in the field of medical imaging which uses videos as their data.
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