Privacy-Preserving Camera-based Monitoring and Tracking System for Parking Spaces

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Zhu, Hanwei
Fan, Songzeng
Wang, Xiyu
Chau, Sid Chi Kin

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Association for Computing Machinery (ACM)

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Abstract

Camera-based tracking systems have been deployed in a wide range of applications. These systems usually aim to infer the temporal and spatial patterns of people and vehicles, rather than identifying them. Nonetheless, there is a substantial concern nowadays over user privacy - the image and video archives of pedestrians and vehicles may expose their identities and behaviors, which lead to unintended criminal consequences. Particularly, hackers may hijack the control of these camera-based tracking systems for malicious purposes. In this work, we explore a privacy-preserving approach by obscuring the camera by a physical blurry filter. We seek to develop an obscured camera-based tracking system that is capable of offering real-time monitoring of parking space vacancies using only low-cost embedded systems (Raspberry Pi). We evaluated the effectiveness of our system at various blurriness levels. Our system demonstrated high accuracy, despite the obstruction by blurry filters.

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BuildSys 2020 - Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

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