How to Label Outdoor Surveillance Data for Computer Vision Models

Outdoor surveillance footage can be effectively utilized in computer vision applications by carefully labeling data to create high-quality datasets for model training. Key practices include using tight bounding boxes to precisely capture objects of interest, labeling occluded objects as if they were fully visible, and choosing thoughtful class names to ensure data usability. Additionally, it is crucial to match training data with production data to enhance model performance. For projects of varying sizes, Roboflow offers an Outsource Labeling service, connecting users with professional labelers who utilize the platform to efficiently annotate datasets. Successful labeling projects typically involve well-documented instructions and a feedback loop with labelers, supported by Roboflow's team to guide labeling strategy and project management.