Privacy Aware Person Detection in Surveillance Data

TL;DR

This paper introduces a privacy-preserving method for person detection in surveillance videos that uses adversarial training to obfuscate video data, enabling accurate detection without compromising privacy.

Contribution

The authors develop a lightweight obfuscator that preserves detection accuracy while protecting individual privacy in surveillance footage.

Findings

Obfuscated videos can be processed by existing detectors without retraining.

The method maintains high detection accuracy despite data obfuscation.

Privacy risks are reduced through adversarially trained obfuscation.

Abstract

Crowd management relies on inspection of surveillance video either by operators or by object detection models. These models are large, making it difficult to deploy them on resource constrained edge hardware. Instead, the computations are often offloaded to a (third party) cloud platform. While crowd management may be a legitimate application, transferring video from the camera to remote infrastructure may open the door for extracting additional information that are infringements of privacy, like person tracking or face recognition. In this paper, we use adversarial training to obtain a lightweight obfuscator that transforms video frames to only retain the necessary information for person detection. Importantly, the obfuscated data can be processed by publicly available object detectors without retraining and without significant loss of accuracy.