Safeguarding Privacy by Reliable Automatic Blurring of Faces in Mobile Mapping Images

When capturing images in the wild containing pedestrians, privacy issues remain a major concern for industrial applications. Our application, collecting cycloramic mobile mapping data in crowded environments, is an example of this. If the data is processed and accessed by third parties, privacy of pedestrians must be ensured. This is where pedestrian detectors come into play, used to detect individuals and privacy mask them through blurring. The problem of undesired false positive detections, typical for pedestrian detectors and unavoidable, still leaves undesired areas of the images being blurred. We tackled this problem using application-specific scene constraints, modelled by a height-position mapping based on scene-specific pedestrian annotation data, combined with reducing the field of interest and case-specific false positive elimination classifiers. We applied a soft blurring technique to avoid the artificial look of simply applying Gaussian blurring to the found detections, which results in an effective fully-automated masking pipeline for privacy safeguarding in mobile mapping images. We prove that we can use pre-trained pedestrian detection models, but by collecting a limited amount of application-specific annotations and by exploiting scene-specific constraints, we are able to boost the detection accuracy enormously.