Tier 3 Video Annotation and Technical Writing Specialist
Video
Bounding BoxThis project focused on creating high-fidelity, large-scale training datasets for machine learning models specializing in egocentric (first-person) action recognition and robotics interaction. The primary scope involved evaluating complex, real-world human tasks—ranging from mechanical operations to fine motor tool handling—and breaking them down into highly granular timestamps. I executed detailed video segmentations, ensuring that transitions between distinct human actions were captured with absolute precision. The core labeling tasks required strict adherence to rigid, low-level taxonomy guidelines, translating physical human workflows into precise 2-word functional baselines (verb-noun pairs). I cross-referenced action tags against highly restricted dictionaries to eliminate "fluff" and ensure compatibility with automated verification scripts. Quality control measures were incredibly strict, requiring a zero-tolerance approach to descriptive ambiguity, structural inconsistencies, or class mismatches. My focus on precise categorization directly contributed to generating clean, optimized datasets designed to train robust AI models to understand human-object interactions seamlessly.