Video Data Annotation for AI and Computer Vision Models
Contributed to AI training projects by performing video data annotation for machine learning and computer vision systems. The work involved reviewing and labeling video datasets by identifying objects, tracking movements across frames, and classifying actions or events according to project guidelines. Tasks included frame-by-frame object tracking, labeling human activities, and categorizing scenes to improve model understanding of motion and temporal patterns. Ensured strict compliance with annotation standards and maintained high consistency across large video datasets. Conducted quality verification checks, corrected labeling inconsistencies, and collaborated with remote teams to maintain dataset accuracy. The project required strong analytical skills, attention to detail, and the ability to efficiently process complex visual data while maintaining high annotation accuracy and reliability for AI model training.