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Videos are collection of image frame, which make s it more challenging to label. Video annotation is the process of labeling video clips necessary for training vision models to detect objects. It involves annotating videos frame by frame.
Labellerr provides multiple type of video annotation. Classification based on single class/multi class or object tracking in a video can be easily handled on our platform. Model-assisted labeling, foundation model based labeling and extrapolating the annotation through the frame can be achieved on our tool. The key video annotation types are:
With semantic segmentation one can label each pixel in a video frame into classes. Everything in the video frame is segmentated, including background features. This helps adding information to every pixel in a piece of video training data.
This annotation type goes one step beyond semantic segmentation by adding more detail to video data. Instance segmentation means that every recurring instance of an object or person is given its unique label and colour. This help to build higher performing AI models.
This is more basic version of video data annotation, in which each object or person get drawn with a rectangular box. It is most common and fastest method to label video frames. It is used in the use case where high level of precision is not required.
This annotation type allows annotators to capture complex shapes. For polygon annotation labelers connect small lines around an object. This allows them to precisely define the shape of any object or person.This method is essential for segmentation methods.
This annotation type helps to show the position of the human body in frames of video. To achieve this technique annotators draw lines on human limbs joined together at joint positions.
This annotation is primarily used to find body/facial features in video frames. Annotators draw points on key area that helps to identify keyjoint.
This type of annotation help to mask object with hollow spaces like a circleor ring.
Labellerr's platform allows ML teams to combine the annotation methods and techniques shown above to create custom video training datasets.
To draw the linear line videoframe for linear structures like roads or pipelines lane annotation techniques can be used. To do lane annotation, labelers draw parallel lines along these structures in each frame of the training video.
Manage video annotation project with comprehensive dashboard to track progress and quality.
Track time spent per file, annotation completed and accuracy in real time for rach annotator
Add consensus between annotator to automatically improve the accuracy, model assisted QA and more
Create a batch run on your data import/export with cloud and save time