Video Annotator
Video
SegmentationWorked as a Video Data Annotator on AI training and computer vision projects with Atlas Capture, contributing to the development and improvement of machine learning models through high-quality video and image annotation. The scope of the project involved annotating large-scale visual datasets used for object detection, activity recognition, motion tracking, and AI model evaluation. The project supported the training of AI systems designed to recognize objects, human actions, and environmental interactions across diverse real-world scenarios. Responsibilities included performing frame-by-frame video annotation, drawing accurate bounding boxes around objects of interest, tracking object movement across multiple frames, labeling actions and activities, and validating annotation consistency according to detailed project guidelines. The work involved handling high-volume datasets consisting of thousands of image and video frames while maintaining annotation precision and meeting productivity targets. Additional tasks included reviewing AI-generated annotations, correcting labeling errors, and ensuring datasets aligned with machine learning training requirements. Strict quality assurance measures were followed throughout the project, including guideline compliance checks, annotation consistency reviews, multi-level quality control processes, and accuracy validation procedures. Attention to detail was critical to ensure precise object localization and reliable training data for AI models. The project required strong analytical skills, time management, and the ability to maintain high-quality annotation standards within fast-paced remote workflows.