![]() ![]() Through annotating, humans teach machines to detect and label objects, actions, and other features by providing examples. Behavior prediction: Predict future movements based on prior behavior.Facial recognition: Identify and markup faces with tags using computer vision.3D camera tracking: Plot 3D information about a scene.Visual search: Tag keypoints of interest, products, or item properties.Motion capture: Tracking and labeling skeleton movements.Action recognition: Recognize and tag actions like "walking," "skipping," and "jumping" in each keypoint.Object identification: Identity, track, and markup objects with computer vision.You can use annotated data for AI in several different ways: Or, if you want to test whether your algorithm can recognize objects in different lighting conditions or from different angles, you can feed it videos with labels showing how the algorithms perform. The prominent use cases are training and evaluating algorithms.įor example, when training an AI model to detect objects in images, you can give examples of what those objects look like with tags that identify them in the frames. There are many uses for annotated data, but one of the most relevant ones is feeding markup training data to a machine learning model for markup. Automated methods tend to be faster and more accurate than manual markup approaches but require significantly larger datasets than manual methods (making them harder to reproduce). There's no limit on how much detail you can put into each frame.Īutomatic labeling involves machine learning to markup frames automatically without any human intervention. It can be tedious for large datasets, but the advantage is that it's fully customizable. Manual human labeling is the most popular approach to labeling each frame. They're a way to train AI, deep learning, and computer vision models. The result is a set of tags for each frame that describe what's happening. ![]() Keypoint annotation applies markup tags to data (usually done by humans). In addition, videos are inherently time-based. There's a beginning and an end, so it's impossible to markup every pixel in a frame. You can't draw out boxes around objects like you can with images. Video annotation is like image labeling but with some crucial differences. ![]()
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