Video Data Annotation
Worked on video annotation tasks using Atlas Capture, focusing on accurately labeling objects and actions across video frames for machine learning datasets. The project involved identifying and tracking relevant entities, applying bounding boxes and tags consistently across sequences, and ensuring frame-level accuracy. Handled datasets of varying complexity, ranging from simple single-object videos to multi-object and motion-based scenarios. Maintained high annotation quality by following strict labeling guidelines, ensuring consistency, and reviewing outputs to reduce errors. Adhered to quality assurance standards including precision checks, frame validation, and guideline compliance to ensure reliable training data for model development.