How low can you go? Privacy-preserving people detection with an omni-directional camera
Timothy Callemein, Kristof Van Beeck, and Toon Goedem\'e

TL;DR
This paper presents a privacy-preserving, low-resolution person detection method using an omni-directional ceiling camera, enabling embedded, low-power occupancy sensing without compromising individual privacy.
Contribution
It introduces a low-resolution person detection network trained with automatically generated ground truth, balancing privacy and detection accuracy, suitable for embedded deployment.
Findings
Effective detection at extremely low resolutions
Lightweight network suitable for embedded hardware
Maintains privacy by preventing individual recognition
Abstract
In this work, we use a ceiling-mounted omni-directional camera to detect people in a room. This can be used as a sensor to measure the occupancy of meeting rooms and count the amount of flex-desk working spaces available. If these devices can be integrated in an embedded low-power sensor, it would form an ideal extension of automated room reservation systems in office environments. The main challenge we target here is ensuring the privacy of the people filmed. The approach we propose is going to extremely low image resolutions, such that it is impossible to recognise people or read potentially confidential documents. Therefore, we retrained a single-shot low-resolution person detection network with automatically generated ground truth. In this paper, we prove the functionality of this approach and explore how low we can go in resolution, to determine the optimal trade-off between…
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