SnA provides exceptional video annotation solutions that support machine learning, artificial intelligence, and data management initiatives.
Video annotation involves labeling or tagging video segments to create training data for computer vision models aimed at object detection and recognition. Unlike image annotation, video annotation requires frame-by-frame labeling to ensure objects are accurately identified by machine learning algorithms. Accurate and high-quality video annotation produces ground truth datasets essential for effective machine learning performance. This technique finds applications in many deep learning fields across various industries, such as autonomous vehicles, medical AI, and geospatial technologies.
Rectangular box annotation is the most prevalent form of video annotation in computer vision. Experts at SnA utilize this technique to mark objects and generate training datasets, enabling applications and algorithms to accurately detect and locate objects throughout machine learning workflows.
Skilled annotators mark points at every vertex of the target object. Polygon annotation enables precise labeling of all the object’s edges, no matter its shape.
The SnA team breaks videos into smaller segments before applying annotations. Their computer vision specialists analyze each video frame carefully, classifying objects at the pixel level.
SnA teams map objects and their shape variations by linking individual points along the objects. This annotation method captures body features and can also represent facial expressions and emotions.
SnA specialists place points on facial landmarks when annotating video footage. Precisely executed landmark annotation generates valuable training data that supports the development of effective computer vision models.
SnA specialists generate training datasets through polyline annotation, helping models learn to recognize physical boundaries within which they function.
SnA specialists carry out object tracking by enclosing objects within cubes. This technique enables systems to determine the object’s length, width, and height.
