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The design of pedestrian-friendly infrastructures plays a crucial role in creating sustainable transportation in urban environments. Analyzing pedestrian behaviour in response to existing infrastructure is pivotal to planning, maintaining, and creating more pedestrian-friendly facilities. Many approaches have been proposed to extract such behaviour by applying deep learning models to video data. Video data, however, includes an broad spectrum of privacy-sensitive information about individuals, such as their location at a given time or who they are with. Most of the existing models use privacy-invasive methodologies to track, detect, and analyse individual or group pedestrian behaviour patterns. As a step towards privacy-preserving pedestrian analysis, this paper introduces a framework to anonymize all pedestrians before analyzing their behaviors. The proposed framework leverages recent developments in 3D wireframe reconstruction and digital in-painting to represent pedestrians with quantitative wireframes by removing their images while preserving pose, shape, and background scene context. To evaluate the proposed framework, a generic metric is introduced for each of privacy and utility. Experimental evaluation on widely-used datasets shows that the proposed framework outperforms traditional and state-of-the-art image filtering approaches by generating best privacy utility trade-off.
Kunchala, A., Bouroche, M., & Schoen-Phelan, B. (2023). Towards A Framework for Privacy-Preserving Pedestrian Analysis. IEEE Xplore. DOI: 10.21427/7VA5-CR03
Science Foundation Ireland