[Customer Success Story] How a Physical-Space Video Analytics Company Built Pedestrian Detection and Tracking Datasets with De-Identified Data

South Korea’s expanding video analytics market faces a tension between demand for AI training data and strict privacy rules governing footage containing identifiable people, making de-identification a practical alternative to using original video. A vision AI company worked with Superb AI to develop pedestrian detection and later video tracking datasets from de-identified images, balancing privacy protection with the visual detail needed for accurate annotation. Superb AI established de-identification standards and labeling guidelines during project kickoff, incorporated the customer’s existing pre-labeling results, annotated pedestrians with bounding boxes and attributes, and converted completed labels into the format required for the customer’s training pipeline. The subsequent tracking project added consistent person IDs across video frames but could proceed quickly because the initial standards and team were already in place. The project produced privacy-compliant, documented training data while allowing the customer’s researchers to focus on model development rather than large-scale manual annotation.

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