A study on the impact of anonymization for training computer vision models with a focus on

This paper investigates the effect of anonymization on training computer vision models using autonomous vehicle datasets.

It uses a combination of real-time and predicted results to assess the effects of anonymizing datasets and improving data quality.

Realistic anonymization performed better than predicted, but improved only slightly: Keypoint detection errors, synthesis limitations, and global context mismatch significantly degraded the results due to keypoint insertion errors, which hampers the application of machine learning in determining the relative merits of different datasets.

To conclude, the study investigated the impact of anonymized data on trainingComputer vision models by evaluating their performance against that of predicted results obtained with no anonymization.

The authors note that while important for complying with privacy regulations, anonymization often reduces data quality, which impeders computer vision development.

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