Video data labeling is the process of labeling video clips. The labeled video data will be used as a training data set for training deep learning and machine learning models. These pre-trained neural networks are then used in the field of computer vision.
What are the advantages of automatic video annotation for training AI models
Similar to image data annotation, video annotation is the process of teaching a computer to recognize objects. Both approaches to data labeling are part of a broader field of artificial intelligence known as computer vision , which aims to train computers to mimic the perceptual qualities of the human eye. In video data labeling projects, human labelers and automated tools are combined to label objects of interest in video footage. This labeled footage is then processed by an AI-powered computer, ideally using machine learning techniques to discover how to identify objects of interest in new, unlabeled videos. The more accurate the video labels, the better the AI model will perform. Precise video annotation with automated tools helps companies deploy with confidence and scale quickly. Watch the video below to learn about video annotation and how it differs from image annotation.
Differences between video and image data annotation
Video annotation has many similarities with image annotation. We describe standard image annotation techniques in our image annotation article , many of which are relevant to applying labels to videos. However, there are significant differences between the two processes that can help companies make a decision if they have to choose between these two data types.
1. Data
Videos have a more complex data structure than images. However, in terms of information per unit of data, video is more insightful. Using the video, the team was able to identify not only the location of the object, but also whether and in which direction the object was moving. For example, images cannot indicate whether a person is sitting down or standing up. But a video will do. Video can also use information from previous frames to identify objects that may be partially occluded. Images do not have this functionality. Taking these factors into account, a video can provide more information per data unit than an image.
2. Labeling process
Compared with image annotation, video annotation is more difficult. Annotators must synchronize and track objects that are constantly changing state from frame to frame. To increase efficiency, many teams use automated process components. Today's computers can track objects across frames without human intervention, so whole video clips can be labeled with less human effort. The end result is that the video annotation process is often much faster than image annotation.
3. Accuracy
When using automated tools to annotate video, there is better continuity from frame to frame and less chance of errors. When labeling multiple images, the same label must be used for the same object, but consistency errors may occur. When annotating a video, a computer can automatically track an object across frames and remember that object with its background throughout the video. Compared with image annotation, this method has higher consistency and accuracy, thereby improving the accuracy of AI model predictions. Given the above factors, companies would prefer video annotation over image annotation, given the choice. The cost of human annotation required for video is much less than that of image annotation, which greatly reduces the annotation time, but has higher accuracy and larger annotation volume. The labeled video data will be used as a training data set for training deep learning and machine learning models. These pre-trained neural networks are then widely used in computer vision. Computer vision is a tool that uses machine learning and deep learning models to process visual data, and is widely used in scenarios such as face recognition, image classification, and automatic video annotation platforms.