High-Precision Multimodal Data Annotation for Computer Vision Model
Video
Point/Key PointWorked on computer vision video annotation projects focused on preparing high-quality datasets for YOLO-based object detection systems, autonomous driving models, and surveillance AI applications. Performed detailed frame-by-frame labeling to support real-world object detection and tracking use cases in dynamic environments. Tasks included drawing precise bounding boxes around vehicles, pedestrians, cyclists, traffic signs, and other relevant objects commonly used in autonomous driving and surveillance datasets. Ensured accurate multi-object tracking across video frames by maintaining consistent tracking IDs and labeling object movement trajectories over time. Applied class-specific annotations aligned with YOLO training requirements, ensuring correct object categorization, spatial accuracy, and frame consistency. Conducted quality checks to detect and correct labeling errors such as ID switching, missed objects, occlusions, and misclassified entities. Followed strict annotation guidelines to ensure dataset reliability for real-world AI systems including traffic monitoring, smart surveillance, and autonomous navigation models. Maintained high precision and consistency across large-scale video datasets to support robust model training and evaluation.
Bounding Box