Video Annotation
The project focused on high-resolution video annotation to support the development of advanced AI models for human action recognition. The goal was to provide detailed, low-level visual descriptions of scenes to enhance model understanding for applications such as behavior tracking, accessibility support, and safety analytics. Specific Data Labeling Tasks Performed: Annotated video clips using a structured format that included: Scene and lighting description Character identification and spatial positioning Object appearance and placement Timestamped action tracking Camera movement descriptions Ensured allocentric directionality and avoided high-level assumptions as per strict annotation protocol. Labeling emotions only when visually verifiable. Maintaining >97% accuracy through layered QA reviews. Adapting to evolving guidelines, document edge cases, and maintain annotation consistency across distributed teams.