Privacy Aware Person Detection in Surveillance Data

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.

References in corpus (6)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Decoupled Weight Decay Regularization
• YOLOv3: An Incremental Improvement
• Gazelle: A Low Latency Framework for Secure Neural Network Inference
• Privacy Aware Offloading of Deep Neural Networks