Privacy Aware Person Detection in Surveillance Data
This addresses privacy concerns in surveillance data processing for crowd management applications, though it is incremental as it builds on existing adversarial training and object detection methods.
The paper tackles the problem of deploying large object detection models on resource-constrained edge hardware by proposing a lightweight obfuscator that transforms video frames to retain only necessary information for person detection, achieving this without significant loss in accuracy when using publicly available detectors.
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.