Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis
2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)(2023)
Abstract
The advent of data-driven technology solutions is accompanied by an increasing concern with data privacy. This is of particular importance for human-centered image recognition tasks, such as pedestrian detection, re-identification, and tracking. To highlight the importance of privacy issues and motivate future research, we motivate and introduce the Pedestrian Dataset De-Identification (PDI) task. PDI evaluates the degree of de-identification and downstream task training performance for a given de-identification method. As a first baseline, we propose IncogniMOT, a two-stage full-body de-identification pipeline based on image synthesis via generative adversarial networks. The first stage replaces target pedestrians with synthetic identities. To improve downstream task performance, we then apply stage two, which blends and adapts the synthetic image parts into the data. To demonstrate the effectiveness of IncogniMOT, we generate a fully de-identified version of the MOT17 pedestrian tracking dataset and analyze its application as training data for pedestrian re-identification, detection, and tracking models. Furthermore, we show how our data is able to narrow the synthetic-to-real performance gap in a privacy-conscious manner.
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Key words
Pedestrian Dataset,Training Data,Data Privacy,Recognition Task,Privacy Issues,Generative Adversarial Networks,Image Recognition,Image Synthesis,Version Of Dataset,Pedestrian Detection,Tracking Dataset,Computer Vision,Detection Performance,Object Detection,Real-world Data,De-identified Data,Bounding Box,Facial Features,Real-world Datasets,Multi-object Tracking,Data Privacy Issues,Laplacian Pyramid,Domain Gap,General Data Protection Regulation,Real Identity,Privacy Guarantee,Relevant Metrics,De-identified Data Set,Body Synthesis
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